General Setup


Create a new analysis directories.

- general directory

- stains directory

- for plots

- for output of summary results

- for baseline tables

- for genetic analyses

- for Cox regression results

source("scripts/functions.R")

source("scripts/pack02.packages.R")


* General packages...

* Genomic packages...

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
source("scripts/colors.R")

Main analyses

Here we perform the main analyses. We first load the prepared RNAseq data, and extract only the relevant genes.

Targets

Here we obtain data from the CONVOCALS_downstream in plaques.

library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/targets.xlsx"), sheet = "Genes")

gene_list <- unlist(gene_list_df$Gene)
gene_list

 [1] "SCGB3A2"  "LIX1"     "IGSF9B"   "ND6"      "ALB"      "OR10A4"   "RASEF"    "NEDD4"    "TRIM49D1" "TCL1A"    "ND4L"     "ATP8"    
[13] "FBXO15"   "F5"       "TMEM212"  "PTPRD"    "CYP46A1"  "OR2T33"   "SORBS2"   "ITGA7"    "RNASE1"   "FOS"      "HMOX1"    "LAPTM5"  
[25] "MMP9"     "SIGLEC1"  "FTL"      "CD14"     "HCST"     "TIMP3"    "CCL2"     "SAT1"     "CD163"    "PTGDS"    "LGALS9"   "ACKR1"   
[37] "NT5DC2"   "TGFBI"    "C1QC"     "S100A9"

Filter genes

In case some genes are not available in our data we could filter them here.

gene_list

 [1] "SCGB3A2"  "LIX1"     "IGSF9B"   "ND6"      "ALB"      "OR10A4"   "RASEF"    "NEDD4"    "TRIM49D1" "TCL1A"    "ND4L"     "ATP8"    
[13] "FBXO15"   "F5"       "TMEM212"  "PTPRD"    "CYP46A1"  "OR2T33"   "SORBS2"   "ITGA7"    "RNASE1"   "FOS"      "HMOX1"    "LAPTM5"  
[25] "MMP9"     "SIGLEC1"  "FTL"      "CD14"     "HCST"     "TIMP3"    "CCL2"     "SAT1"     "CD163"    "PTGDS"    "LGALS9"   "ACKR1"   
[37] "NT5DC2"   "TGFBI"    "C1QC"     "S100A9"

This is to filter the list from genes that are not in the data.

ND6 ==> MTND6, not found
TRIM49D1 ==> TRIM49DP, not found
ND4L ==> MTND4L, not found
ATP8 ==> MTATP8, not found
RNASE1 ==> RNS1, not found


gene_list_rm <- c("ND6", "TRIM49D1", "ND4L", "ATP8", "RNASE1")

temp = gene_list[!gene_list %in% gene_list_rm]

gene_list_qc <- c(temp)

# gene_list_qc <- gene_list
# 
# for debug
# gene_list_qc_replace <- c("MRTFA")

Loading data

We simply load the previously saved RDS-file and extract the clinical and RNAseq data from that.

# AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.CEA.RDS"))
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20241017.",PROJECTNAME,".AEDB.CEA.RDS"))
AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)

[1] "ae1" "ae2" "ae3" "ae4" "ae5" "ae6"

# AERNASE <- readRDS(file = paste0(OUT_loc, "/", Today, ".AERNAScomboSEvst.CEA.1092pts.SE.after_qc.IC_academic.RDS"))
AERNASE <- readRDS(file = paste0(OUT_loc, "/20241017.AERNAScomboSEvst.CEA.1092pts.SE.after_qc.IC_academic.RDS"))

# AERNASE.targets <- readRDS(file = paste0(OUT_loc, "/", Today, ".AERNAScomboSE_target.CEA.1092pts.SE.after_qc.IC_academic.RDS"))
AERNASE.targets <- readRDS(file = paste0(OUT_loc, "/20241017.AERNAScomboSE_target.CEA.1092pts.SE.after_qc.IC_academic.RDS"))

row.names(AERNASE.targets) <- rowData(AERNASE.targets)$symbol
temp <- as_tibble(t(assay(AERNASE.targets)), rownames = "study_number")

AERNASE.clin.targets <- as.data.frame(merge(colData(AERNASE.targets), temp, by.x = "study_number", by.y = "study_number", sort = TRUE))
rm(temp)
dim(AERNASE.clin.targets)

[1] 1092  108

head(AERNASE.clin.targets)

Getting some summary statistics.

cat("Average expression of a random selection of 1000 genes.\n")

Average expression of a random selection of 1000 genes.

set.seed(141619)
mean(rowMeans(as_tibble(assay(AERNASE)) %>% dplyr::sample_n(1000)))

[1] 5.046551

cat("\nAverage expression of target genes.\n")


Average expression of target genes.

rowMeans(assay(AERNASE.targets))

  CYP46A1     NEDD4    IGSF9B       FTL   SIGLEC1     TIMP3     HMOX1     TCL1A      MMP9     PTGDS      CCL2     TGFBI      HCST 
 8.218635  8.797541  6.060812  9.569224  5.505736  6.453179  7.368089  8.609284  6.500984  4.886650  6.372147  6.459441  7.043525 
   RNASE1      SAT1     ITGA7    FBXO15      LIX1     PTPRD    SORBS2      C1QC    LAPTM5    S100A9       ALB   SCGB3A2     RASEF 
 7.887292  7.724931  8.280004  8.714241  7.958918  4.569583  7.634092  6.668880  6.783501  4.597477  6.142387  5.596779  7.457387 
   NT5DC2    LGALS9       FOS      CD14    OR10A4    OR2T33     CD163   TMEM212       ND6        F5      ND4L     ACKR1  TRIM49D1 
 5.550996  6.428877  6.233012  6.832575  5.634484  6.984607  6.028195  8.094305 13.064240  6.285768 10.228166  4.606290  6.952308 
     ATP8 
11.424358

cat("\nGene information.\n")


Gene information.

rowRanges(AERNASE.targets)

GRanges object with 40 ranges and 2 metadata columns:
           seqnames              ranges strand |      feature_id      symbol
              <Rle>           <IRanges>  <Rle> |     <character> <character>
   CYP46A1       14   99684298-99727301      + | ENSG00000036530     CYP46A1
     NEDD4       15   55826922-55993660      - | ENSG00000069869       NEDD4
    IGSF9B       11 133896438-133956968      - | ENSG00000080854      IGSF9B
       FTL       19   48965309-48966879      + | ENSG00000087086         FTL
   SIGLEC1       20     3686970-3712600      - | ENSG00000088827     SIGLEC1
       ...      ...                 ...    ... .             ...         ...
        F5        1 169511951-169586588      - | ENSG00000198734          F5
      ND4L       MT         10470-10766      + | ENSG00000212907        ND4L
     ACKR1        1 159203307-159206500      + | ENSG00000213088       ACKR1
  TRIM49D1       11   89911111-89922245      - | ENSG00000223417    TRIM49D1
      ATP8       MT           8366-8572      + | ENSG00000228253        ATP8
  -------
  seqinfo: 331 sequences from an unspecified genome; no seqlengths

AERNASE.targets.genedata <- as_tibble(rowRanges(AERNASE.targets))

AERNASE.targets.genedata

Analyses

The analyses are focused on three elements:

plaque vulnerability phenotypes
clinical status at inclusion (symptoms)
secondary clinical outcome during three (3) years of follow-up

Covariates & other variables

Age (continuous in 1-year increment). [Age]
Sex (male vs. female). [Gender]
Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [DiabetesStatus]
Smoking (current, ex-, never). [SmokerStatus]
LDL-C levels (continuous). [LDL_final]
Use of lipid-lowering drugs. [Med.Statin.LLD]
Use of antiplatelet drugs. [Med.all.antiplatelet]
eGFR (continuous). [GFR_MDRD]
BMI (continuous). [BMI]
History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combination of [CAD_history, Stroke_history, Peripheral.interv]
Level of stenosis (50-70% vs. 70-99%). [stenose]
Year of surgery [ORdate_year] as we discovered in Van Lammeren et al. the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

Models

We will analyze the data through four different models

Model 1: adjusted for age, sex, and year of surgery
Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque CONVOCALS_downstream levels we will focus on the following plaque vulnerability phenotypes:

Percentage of macrophages (continuous trait)
Percentage of SMCs (continuous trait)
Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
Presence of moderate/heavy calcifications (binary trait)
Presence of moderate/heavy collagen content (binary trait)
Presence of lipid core no/<10% vs. >10% (binary trait)
Presence of intraplaque hemorrhage (binary trait)

Quantitative traits

We inspect the plaque characteristics, and inverse-rank normal transformation continuous phenotypes.


# macrophages
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$MAC_rankNorm)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.0937 -0.3743  0.2632  0.2454  0.8887  2.9965     223

min_macmean <- min(AERNASE.clin.targets$MAC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))


Minimum value % macrophages: -2.09371245439672.

ggpubr::gghistogram(AERNASE.clin.targets, "MAC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.

# smooth muscle cells
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$SMC_rankNorm)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
-2.64373 -0.67572 -0.05778 -0.06312  0.57743  2.75568      225

min_smcmean <- min(AERNASE.clin.targets$SMC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))


Minimum value % smooth muscle cells: -2.64372511353808.

ggpubr::gghistogram(AERNASE.clin.targets, "SMC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.

# vessel density
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$VesselDensity_rankNorm)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
-1.92028 -0.58375  0.08982  0.08929  0.76945  3.17029      280

min_vesseldensity <- min(AERNASE.clin.targets$VesselDensity_rankNorm, na.rm = TRUE)
min_vesseldensity

[1] -1.920282

cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))


Minimum value number of intraplaque neovessels per 3-4 hotspots: -1.92028163343836.

ggpubr::gghistogram(AERNASE.clin.targets, "VesselDensity_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())

Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed (‘unstable’) as compared to ‘stable’.


AERNASE.clin.targets$MAC_SMC_ratio <- AERNASE.clin.targets$MAC_rankNorm / AERNASE.clin.targets$SMC_rankNorm

AERNASE.clin.targets$MAC_SMC_ratio_rank  <- qnorm((rank(AERNASE.clin.targets$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.targets$MAC_SMC_ratio)))


cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$MAC_rankNorm)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.0937 -0.3743  0.2632  0.2454  0.8887  2.9965     223

summary(AERNASE.clin.targets$SMC_rankNorm)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
-2.64373 -0.67572 -0.05778 -0.06312  0.57743  2.75568      225

summary(AERNASE.clin.targets$MAC_SMC_ratio_rank)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-3.2498 -0.6736  0.0000  0.0000  0.6736  3.2498     226

ggpubr::gghistogram(AERNASE.clin.targets, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())

Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.

Binary traits

library(dplyr)
# calcification
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$Calc.bin)

      no/minor moderate/heavy           NA's 
           497            461            134

contrasts(AERNASE.clin.targets$Calc.bin)

               moderate/heavy
no/minor                    0
moderate/heavy              1

AERNASE.clin.targets$CalcificationPlaque <- as.factor(AERNASE.clin.targets$Calc.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(CalcificationPlaque)) %>%
  dplyr::group_by(Gender, CalcificationPlaque) %>%
dplyr::summarise(counts = n())

`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())

rm(df)

# collagen
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$Collagen.bin)

      no/minor moderate/heavy           NA's 
           192            740            160

contrasts(AERNASE.clin.targets$Collagen.bin)

               moderate/heavy
no/minor                    0
moderate/heavy              1

AERNASE.clin.targets$CollagenPlaque <- as.factor(AERNASE.clin.targets$Collagen.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(CollagenPlaque)) %>%
  dplyr::group_by(Gender, CollagenPlaque) %>%
dplyr::summarise(counts = n())

`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())

rm(df)

# fat 10%
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$Fat.bin_10)

 <10%  >10%  NA's 
  244   714   134

contrasts(AERNASE.clin.targets$Fat.bin_10)

       >10%
 <10%     0
 >10%     1

AERNASE.clin.targets$Fat10Perc <- as.factor(AERNASE.clin.targets$Fat.bin_10)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(Fat10Perc)) %>%
  dplyr::group_by(Gender, Fat10Perc) %>%
dplyr::summarise(counts = n())

`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())

rm(df)

# macrophages binned
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$Macrophages.bin)

      no/minor moderate/heavy           NA's 
           407            518            167

contrasts(AERNASE.clin.targets$Macrophages.bin)

               moderate/heavy
no/minor                    0
moderate/heavy              1

AERNASE.clin.targets$MAC_binned <- as.factor(AERNASE.clin.targets$Macrophages.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(MAC_binned)) %>%
  dplyr::group_by(Gender, MAC_binned) %>%
dplyr::summarise(counts = n())

`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())

rm(df)



# SMC binned
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$SMC.bin)

      no/minor moderate/heavy           NA's 
           308            618            166

contrasts(AERNASE.clin.targets$SMC.bin)

               moderate/heavy
no/minor                    0
moderate/heavy              1

AERNASE.clin.targets$SMC_binned <- as.factor(AERNASE.clin.targets$SMC.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(SMC_binned)) %>%
  dplyr::group_by(Gender, SMC_binned) %>%
dplyr::summarise(counts = n())

`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())

rm(df)




# IPH
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$IPH.bin)

  no  yes NA's 
 370  564  158

contrasts(AERNASE.clin.targets$IPH.bin)

    yes
no    0
yes   1

AERNASE.clin.targets$IPH <- as.factor(AERNASE.clin.targets$IPH.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(IPH)) %>%
  dplyr::group_by(Gender, IPH) %>%
dplyr::summarise(counts = n())

`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())

rm(df)

# Symptoms
cat("Summary of data.\n")

Summary of data.

summary(AERNASE.clin.targets$AsymptSympt)

     Asymptomatic Ocular and others       Symptomatic 
              111               256               725

contrasts(AERNASE.clin.targets$AsymptSympt)

                  Ocular and others Symptomatic
Asymptomatic                      0           0
Ocular and others                 1           0
Symptomatic                       0           1

AERNASE.clin.targets$AsymptSympt <- as.factor(AERNASE.clin.targets$AsymptSympt)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(AsymptSympt)) %>%
  dplyr::group_by(Gender, AsymptSympt) %>%
dplyr::summarise(counts = n())

`summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())

rm(df)

Prepare modeling

In this section we make some variables to assist with analysis.

We fix the diabetes status variable.


# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"DiabetesStatus"] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == -999] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB.CEA$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB.CEA)

table(AEDB.CEA$DM.composite, AEDB.CEA$DiabetesStatus)

   
    Control (no Diabetes Dx/Med) Diabetes
  0                         1972        0
  1                            0      607

# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$DiabetesStatus <- to_factor(AEDB.CEA.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv

# AEDB.CEA$CAD_history
# AEDB.CEA$Stroke_history
# AEDB.CEA$Peripheral.interv

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"MedHx_CVD"] <- NA
AEDB.CEA$MedHx_CVD[CAD_history == "No history CAD" | Stroke_history == 0 | Peripheral.interv == "no"] <- "No"
AEDB.CEA$MedHx_CVD[CAD_history == "History CAD" | Stroke_history == 1 | Peripheral.interv == "yes"] <- "yes"
detach(AEDB.CEA)

table(AEDB.CEA$CAD_history)


       Missing No history CAD    History CAD 
             0           1759            818

table(AEDB.CEA$Stroke_history)


   0    1 
1755  830

table(AEDB.CEA$Peripheral.interv)


  no  yes 
2030  539

table(AEDB.CEA$MedHx_CVD)


  No  yes 
 939 1646

# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$AlcoholUse <- to_factor(AEDB.CEA.temp$AlcoholUse)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)

AERNASE.clin.targets$DiabetesStatus <- NULL
AERNASE.clin.targets$MedHx_CVD <- NULL

AERNASE.clin.targets <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "dateok", "DiabetesStatus", "MedHx_CVD")), 
                            by.x = "study_number", by.y = "STUDY_NUMBER", sort = TRUE)

AERNASE.clin.targets$MedHx_CVD <- as_factor(AERNASE.clin.targets$MedHx_CVD)
AERNASE.clin.targets$DiabetesStatus <- as_factor(AERNASE.clin.targets$DiabetesStatus)


study.samplesize = nrow(AERNASE.clin.targets) # study.samplesize is an expected variable in the GLM.BIN() GLM.CON() functions

TRAITS.TARGET.RANK = c(gene_list_qc)

TRAITS.CON.RANK = c("MAC_rankNorm", "SMC_rankNorm", "MAC_SMC_ratio_rank", "VesselDensity_rankNorm")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.   BMI (continuous). [BMI]
# 11.   History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.   Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AERNASE.clin.targets$ORdate_epoch <- as.numeric(AERNASE.clin.targets$dateok)
AERNASE.clin.targets$ORdate_year <- AERNASE.clin.targets$ORyear
AERNASE.clin.targets$StudyName <- to_factor(AERNASE.clin.targets$StudyName)

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")

Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`

summary(AERNASE.clin.targets$ORdate_epoch)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  11770   13125   14119   14244   15386   17493

cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")


Summary of 'year of surgery' in 'years' (); coded as `factor()`

table(AERNASE.clin.targets$ORdate_year)


No data available/missing                      2002                      2003                      2004                      2005 
                        0                        38                        67                        77                        97 
                     2006                      2007                      2008                      2009                      2010 
                      105                        97                        94                       104                        75 
                     2011                      2012                      2013                      2014                      2015 
                       59                        67                        70                        72                        30 
                     2016                      2017                      2018                      2019                      2020 
                       28                        12                         0                         0                         0 
                     2021                      2022 
                        0                         0

# COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch", "StudyName")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")
# str(AERNASE.clin.targets)
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

Model 1

In this model we correct for Age, Gender, and year of surgery.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

Analysis of continuous/quantitative plaque traits as a function of plaque CONVOCALS_downstream expression levels.

library(dplyr)
library(MASS)

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))

cat("Running linear regression...\n")

Running linear regression...

for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    # print(model_step)
    # print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}


Analysis of SCGB3A2.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.01799 
Standard error............: 0.046054 
Odds ratio (effect size)..: 1.018 
Lower 95% CI..............: 0.93 
Upper 95% CI..............: 1.114 
T-value...................: 0.390622 
P-value...................: 0.6961729 
R^2.......................: 0.039519 
Adjusted r^2..............: 0.035072 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.181628 
Standard error............: 0.047409 
Odds ratio (effect size)..: 1.199 
Lower 95% CI..............: 1.093 
Upper 95% CI..............: 1.316 
T-value...................: 3.831131 
P-value...................: 0.0001368094 
R^2.......................: 0.055483 
Adjusted r^2..............: 0.0511 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.019043 
Standard error............: 0.042244 
Odds ratio (effect size)..: 0.981 
Lower 95% CI..............: 0.903 
Upper 95% CI..............: 1.066 
T-value...................: -0.450789 
P-value...................: 0.6522554 
R^2.......................: 0.040038 
Adjusted r^2..............: 0.035578 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.005692 
Standard error............: 0.046455 
Odds ratio (effect size)..: 1.006 
Lower 95% CI..............: 0.918 
Upper 95% CI..............: 1.102 
T-value...................: 0.122529 
P-value...................: 0.9025104 
R^2.......................: 0.037958 
Adjusted r^2..............: 0.033189 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of LIX1.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.075852 
Standard error............: 0.033596 
Odds ratio (effect size)..: 0.927 
Lower 95% CI..............: 0.868 
Upper 95% CI..............: 0.99 
T-value...................: -2.257813 
P-value...................: 0.02420618 
R^2.......................: 0.029326 
Adjusted r^2..............: 0.024832 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.16833 
Standard error............: 0.034404 
Odds ratio (effect size)..: 1.183 
Lower 95% CI..............: 1.106 
Upper 95% CI..............: 1.266 
T-value...................: 4.89278 
P-value...................: 1.186341e-06 
R^2.......................: 0.050067 
Adjusted r^2..............: 0.045659 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.033062 
Standard error............: 0.030847 
Odds ratio (effect size)..: 1.034 
Lower 95% CI..............: 0.973 
Upper 95% CI..............: 1.098 
T-value...................: 1.071804 
P-value...................: 0.2841082 
R^2.......................: 0.024986 
Adjusted r^2..............: 0.020457 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.037287 
Standard error............: 0.033548 
Odds ratio (effect size)..: 1.038 
Lower 95% CI..............: 0.972 
Upper 95% CI..............: 1.109 
T-value...................: 1.111449 
P-value...................: 0.2667063 
R^2.......................: 0.025747 
Adjusted r^2..............: 0.020918 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of IGSF9B.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.045738 
Standard error............: 0.045501 
Odds ratio (effect size)..: 1.047 
Lower 95% CI..............: 0.957 
Upper 95% CI..............: 1.144 
T-value...................: 1.005198 
P-value...................: 0.315083 
R^2.......................: 0.039292 
Adjusted r^2..............: 0.034844 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.190025 
Standard error............: 0.046829 
Odds ratio (effect size)..: 1.209 
Lower 95% CI..............: 1.103 
Upper 95% CI..............: 1.326 
T-value...................: 4.057814 
P-value...................: 5.403206e-05 
R^2.......................: 0.056203 
Adjusted r^2..............: 0.051824 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.029395 
Standard error............: 0.041764 
Odds ratio (effect size)..: 0.971 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.054 
T-value...................: -0.703845 
P-value...................: 0.4817195 
R^2.......................: 0.039039 
Adjusted r^2..............: 0.034575 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.012322 
Standard error............: 0.045543 
Odds ratio (effect size)..: 0.988 
Lower 95% CI..............: 0.903 
Upper 95% CI..............: 1.08 
T-value...................: -0.270551 
P-value...................: 0.7868056 
R^2.......................: 0.039382 
Adjusted r^2..............: 0.034621 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of ALB.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.033195 
Standard error............: 0.037057 
Odds ratio (effect size)..: 0.967 
Lower 95% CI..............: 0.9 
Upper 95% CI..............: 1.04 
T-value...................: -0.895779 
P-value...................: 0.3706206 
R^2.......................: 0.014282 
Adjusted r^2..............: 0.009718 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.146313 
Standard error............: 0.038167 
Odds ratio (effect size)..: 1.158 
Lower 95% CI..............: 1.074 
Upper 95% CI..............: 1.247 
T-value...................: 3.833487 
P-value...................: 0.0001355271 
R^2.......................: 0.03016 
Adjusted r^2..............: 0.02566 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.077312 
Standard error............: 0.033866 
Odds ratio (effect size)..: 0.926 
Lower 95% CI..............: 0.866 
Upper 95% CI..............: 0.989 
T-value...................: -2.28291 
P-value...................: 0.02267866 
R^2.......................: 0.01991 
Adjusted r^2..............: 0.015357 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.039244 
Standard error............: 0.037221 
Odds ratio (effect size)..: 0.962 
Lower 95% CI..............: 0.894 
Upper 95% CI..............: 1.034 
T-value...................: -1.054367 
P-value...................: 0.2920303 
R^2.......................: 0.020859 
Adjusted r^2..............: 0.016005 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of OR10A4.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.040967 
Standard error............: 0.039797 
Odds ratio (effect size)..: 0.96 
Lower 95% CI..............: 0.888 
Upper 95% CI..............: 1.038 
T-value...................: -1.029401 
P-value...................: 0.3035793 
R^2.......................: 0.008564 
Adjusted r^2..............: 0.003974 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.154687 
Standard error............: 0.040988 
Odds ratio (effect size)..: 1.167 
Lower 95% CI..............: 1.077 
Upper 95% CI..............: 1.265 
T-value...................: 3.773946 
P-value...................: 0.0001716837 
R^2.......................: 0.023586 
Adjusted r^2..............: 0.019055 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.093099 
Standard error............: 0.036364 
Odds ratio (effect size)..: 0.911 
Lower 95% CI..............: 0.848 
Upper 95% CI..............: 0.978 
T-value...................: -2.560224 
P-value...................: 0.01063007 
R^2.......................: 0.015145 
Adjusted r^2..............: 0.01057 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.023302 
Standard error............: 0.03988 
Odds ratio (effect size)..: 0.977 
Lower 95% CI..............: 0.904 
Upper 95% CI..............: 1.056 
T-value...................: -0.58429 
P-value...................: 0.5591887 
R^2.......................: 0.012449 
Adjusted r^2..............: 0.007555 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of RASEF.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.04048 
Standard error............: 0.03731 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.968 
Upper 95% CI..............: 1.12 
T-value...................: 1.084969 
P-value...................: 0.278238 
R^2.......................: 0.040674 
Adjusted r^2..............: 0.036232 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.153066 
Standard error............: 0.038348 
Odds ratio (effect size)..: 1.165 
Lower 95% CI..............: 1.081 
Upper 95% CI..............: 1.256 
T-value...................: 3.991539 
P-value...................: 7.123374e-05 
R^2.......................: 0.057533 
Adjusted r^2..............: 0.05316 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.006068 
Standard error............: 0.0342 
Odds ratio (effect size)..: 0.994 
Lower 95% CI..............: 0.93 
Upper 95% CI..............: 1.063 
T-value...................: -0.177429 
P-value...................: 0.8592134 
R^2.......................: 0.040528 
Adjusted r^2..............: 0.03607 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.004509 
Standard error............: 0.037707 
Odds ratio (effect size)..: 0.996 
Lower 95% CI..............: 0.925 
Upper 95% CI..............: 1.072 
T-value...................: -0.119591 
P-value...................: 0.9048368 
R^2.......................: 0.040129 
Adjusted r^2..............: 0.035371 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of NEDD4.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.00625 
Standard error............: 0.035705 
Odds ratio (effect size)..: 1.006 
Lower 95% CI..............: 0.938 
Upper 95% CI..............: 1.079 
T-value...................: 0.175057 
P-value...................: 0.8610762 
R^2.......................: 0.017462 
Adjusted r^2..............: 0.012913 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.131234 
Standard error............: 0.036794 
Odds ratio (effect size)..: 1.14 
Lower 95% CI..............: 1.061 
Upper 95% CI..............: 1.226 
T-value...................: 3.566748 
P-value...................: 0.0003813519 
R^2.......................: 0.032149 
Adjusted r^2..............: 0.027658 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.050527 
Standard error............: 0.032669 
Odds ratio (effect size)..: 0.951 
Lower 95% CI..............: 0.892 
Upper 95% CI..............: 1.014 
T-value...................: -1.546633 
P-value...................: 0.1223191 
R^2.......................: 0.020932 
Adjusted r^2..............: 0.016384 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.009867 
Standard error............: 0.036018 
Odds ratio (effect size)..: 0.99 
Lower 95% CI..............: 0.923 
Upper 95% CI..............: 1.063 
T-value...................: -0.273952 
P-value...................: 0.7841918 
R^2.......................: 0.020621 
Adjusted r^2..............: 0.015766 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of TCL1A.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.09169 
Standard error............: 0.030882 
Odds ratio (effect size)..: 0.912 
Lower 95% CI..............: 0.859 
Upper 95% CI..............: 0.969 
T-value...................: -2.969013 
P-value...................: 0.003070145 
R^2.......................: 0.045376 
Adjusted r^2..............: 0.040957 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.135419 
Standard error............: 0.031839 
Odds ratio (effect size)..: 1.145 
Lower 95% CI..............: 1.076 
Upper 95% CI..............: 1.219 
T-value...................: 4.25328 
P-value...................: 2.337017e-05 
R^2.......................: 0.056027 
Adjusted r^2..............: 0.051646 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.035532 
Standard error............: 0.028441 
Odds ratio (effect size)..: 1.036 
Lower 95% CI..............: 0.98 
Upper 95% CI..............: 1.096 
T-value...................: 1.24932 
P-value...................: 0.2118875 
R^2.......................: 0.037962 
Adjusted r^2..............: 0.033493 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.037622 
Standard error............: 0.030912 
Odds ratio (effect size)..: 1.038 
Lower 95% CI..............: 0.977 
Upper 95% CI..............: 1.103 
T-value...................: 1.217089 
P-value...................: 0.2239261 
R^2.......................: 0.038145 
Adjusted r^2..............: 0.033378 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of FBXO15.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.025578 
Standard error............: 0.035822 
Odds ratio (effect size)..: 0.975 
Lower 95% CI..............: 0.909 
Upper 95% CI..............: 1.046 
T-value...................: -0.714033 
P-value...................: 0.4753994 
R^2.......................: 0.014739 
Adjusted r^2..............: 0.010177 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.139332 
Standard error............: 0.036893 
Odds ratio (effect size)..: 1.15 
Lower 95% CI..............: 1.069 
Upper 95% CI..............: 1.236 
T-value...................: 3.776686 
P-value...................: 0.0001698369 
R^2.......................: 0.030284 
Adjusted r^2..............: 0.025784 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.042601 
Standard error............: 0.032817 
Odds ratio (effect size)..: 0.958 
Lower 95% CI..............: 0.899 
Upper 95% CI..............: 1.022 
T-value...................: -1.298163 
P-value...................: 0.1945789 
R^2.......................: 0.016507 
Adjusted r^2..............: 0.011938 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.017621 
Standard error............: 0.035978 
Odds ratio (effect size)..: 0.983 
Lower 95% CI..............: 0.916 
Upper 95% CI..............: 1.054 
T-value...................: -0.489774 
P-value...................: 0.6244272 
R^2.......................: 0.01844 
Adjusted r^2..............: 0.013575 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of F5.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.020515 
Standard error............: 0.035478 
Odds ratio (effect size)..: 0.98 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.05 
T-value...................: -0.57825 
P-value...................: 0.5632461 
R^2.......................: 0.018894 
Adjusted r^2..............: 0.014352 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.161335 
Standard error............: 0.036458 
Odds ratio (effect size)..: 1.175 
Lower 95% CI..............: 1.094 
Upper 95% CI..............: 1.262 
T-value...................: 4.425201 
P-value...................: 1.087008e-05 
R^2.......................: 0.040593 
Adjusted r^2..............: 0.036141 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.043202 
Standard error............: 0.032508 
Odds ratio (effect size)..: 0.958 
Lower 95% CI..............: 0.899 
Upper 95% CI..............: 1.021 
T-value...................: -1.328957 
P-value...................: 0.1842141 
R^2.......................: 0.021207 
Adjusted r^2..............: 0.01666 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.011728 
Standard error............: 0.035864 
Odds ratio (effect size)..: 0.988 
Lower 95% CI..............: 0.921 
Upper 95% CI..............: 1.06 
T-value...................: -0.327006 
P-value...................: 0.7437484 
R^2.......................: 0.021638 
Adjusted r^2..............: 0.016789 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of TMEM212.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.018474 
Standard error............: 0.034356 
Odds ratio (effect size)..: 0.982 
Lower 95% CI..............: 0.918 
Upper 95% CI..............: 1.05 
T-value...................: -0.537715 
P-value...................: 0.5909122 
R^2.......................: 0.014889 
Adjusted r^2..............: 0.010328 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.166111 
Standard error............: 0.03522 
Odds ratio (effect size)..: 1.181 
Lower 95% CI..............: 1.102 
Upper 95% CI..............: 1.265 
T-value...................: 4.716422 
P-value...................: 2.798836e-06 
R^2.......................: 0.039578 
Adjusted r^2..............: 0.035121 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.067831 
Standard error............: 0.031405 
Odds ratio (effect size)..: 0.934 
Lower 95% CI..............: 0.879 
Upper 95% CI..............: 0.994 
T-value...................: -2.1599 
P-value...................: 0.03105584 
R^2.......................: 0.020433 
Adjusted r^2..............: 0.015882 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.026741 
Standard error............: 0.034505 
Odds ratio (effect size)..: 0.974 
Lower 95% CI..............: 0.91 
Upper 95% CI..............: 1.042 
T-value...................: -0.775006 
P-value...................: 0.438563 
R^2.......................: 0.01972 
Adjusted r^2..............: 0.014861 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of PTPRD.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.01693 
Standard error............: 0.020546 
Odds ratio (effect size)..: 0.983 
Lower 95% CI..............: 0.944 
Upper 95% CI..............: 1.024 
T-value...................: -0.824006 
P-value...................: 0.4101637 
R^2.......................: 0.003574 
Adjusted r^2..............: -0.001039 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.024803 
Standard error............: 0.021258 
Odds ratio (effect size)..: 1.025 
Lower 95% CI..............: 0.983 
Upper 95% CI..............: 1.069 
T-value...................: 1.166724 
P-value...................: 0.2436446 
R^2.......................: 0.00481 
Adjusted r^2..............: 0.000192 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.021921 
Standard error............: 0.01881 
Odds ratio (effect size)..: 0.978 
Lower 95% CI..............: 0.943 
Upper 95% CI..............: 1.015 
T-value...................: -1.165414 
P-value...................: 0.2441741 
R^2.......................: 0.004786 
Adjusted r^2..............: 0.000163 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.03714 
Standard error............: 0.020427 
Odds ratio (effect size)..: 0.964 
Lower 95% CI..............: 0.926 
Upper 95% CI..............: 1.003 
T-value...................: -1.818206 
P-value...................: 0.06940321 
R^2.......................: 0.009951 
Adjusted r^2..............: 0.005044 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of CYP46A1.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.021514 
Standard error............: 0.035752 
Odds ratio (effect size)..: 0.979 
Lower 95% CI..............: 0.912 
Upper 95% CI..............: 1.05 
T-value...................: -0.601757 
P-value...................: 0.5474935 
R^2.......................: 0.00976 
Adjusted r^2..............: 0.005175 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.142245 
Standard error............: 0.036797 
Odds ratio (effect size)..: 1.153 
Lower 95% CI..............: 1.073 
Upper 95% CI..............: 1.239 
T-value...................: 3.865623 
P-value...................: 0.0001191293 
R^2.......................: 0.026087 
Adjusted r^2..............: 0.021567 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.062831 
Standard error............: 0.03272 
Odds ratio (effect size)..: 0.939 
Lower 95% CI..............: 0.881 
Upper 95% CI..............: 1.001 
T-value...................: -1.920252 
P-value...................: 0.05515617 
R^2.......................: 0.013683 
Adjusted r^2..............: 0.0091 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.019511 
Standard error............: 0.035917 
Odds ratio (effect size)..: 0.981 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.052 
T-value...................: -0.543222 
P-value...................: 0.5871268 
R^2.......................: 0.013909 
Adjusted r^2..............: 0.009022 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of OR2T33.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.009338 
Standard error............: 0.036839 
Odds ratio (effect size)..: 1.009 
Lower 95% CI..............: 0.939 
Upper 95% CI..............: 1.085 
T-value...................: 0.253486 
P-value...................: 0.7999533 
R^2.......................: 0.016579 
Adjusted r^2..............: 0.012026 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.139074 
Standard error............: 0.037921 
Odds ratio (effect size)..: 1.149 
Lower 95% CI..............: 1.067 
Upper 95% CI..............: 1.238 
T-value...................: 3.667506 
P-value...................: 0.0002599413 
R^2.......................: 0.031905 
Adjusted r^2..............: 0.027413 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.063112 
Standard error............: 0.033688 
Odds ratio (effect size)..: 0.939 
Lower 95% CI..............: 0.879 
Upper 95% CI..............: 1.003 
T-value...................: -1.873438 
P-value...................: 0.06134649 
R^2.......................: 0.021041 
Adjusted r^2..............: 0.016493 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.024126 
Standard error............: 0.037102 
Odds ratio (effect size)..: 0.976 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.05 
T-value...................: -0.650262 
P-value...................: 0.5157083 
R^2.......................: 0.0197 
Adjusted r^2..............: 0.014841 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of SORBS2.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.091982 
Standard error............: 0.038024 
Odds ratio (effect size)..: 0.912 
Lower 95% CI..............: 0.847 
Upper 95% CI..............: 0.983 
T-value...................: -2.419052 
P-value...................: 0.01576667 
R^2.......................: 0.018507 
Adjusted r^2..............: 0.013963 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.088333 
Standard error............: 0.039342 
Odds ratio (effect size)..: 1.092 
Lower 95% CI..............: 1.011 
Upper 95% CI..............: 1.18 
T-value...................: 2.245292 
P-value...................: 0.02500215 
R^2.......................: 0.016908 
Adjusted r^2..............: 0.012346 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.058604 
Standard error............: 0.034862 
Odds ratio (effect size)..: 0.943 
Lower 95% CI..............: 0.881 
Upper 95% CI..............: 1.01 
T-value...................: -1.681048 
P-value...................: 0.09311619 
R^2.......................: 0.014381 
Adjusted r^2..............: 0.009802 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.052415 
Standard error............: 0.037867 
Odds ratio (effect size)..: 0.949 
Lower 95% CI..............: 0.881 
Upper 95% CI..............: 1.022 
T-value...................: -1.384179 
P-value...................: 0.1666862 
R^2.......................: 0.016551 
Adjusted r^2..............: 0.011677 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of ITGA7.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.027272 
Standard error............: 0.031921 
Odds ratio (effect size)..: 0.973 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.036 
T-value...................: -0.854357 
P-value...................: 0.3931442 
R^2.......................: 0.018409 
Adjusted r^2..............: 0.013865 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.108877 
Standard error............: 0.032972 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 1.045 
Upper 95% CI..............: 1.189 
T-value...................: 3.302145 
P-value...................: 0.0009989609 
R^2.......................: 0.030189 
Adjusted r^2..............: 0.025689 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.064115 
Standard error............: 0.029178 
Odds ratio (effect size)..: 0.938 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 0.993 
T-value...................: -2.19739 
P-value...................: 0.02825826 
R^2.......................: 0.023828 
Adjusted r^2..............: 0.019293 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.019798 
Standard error............: 0.031959 
Odds ratio (effect size)..: 0.98 
Lower 95% CI..............: 0.921 
Upper 95% CI..............: 1.044 
T-value...................: -0.619466 
P-value...................: 0.5357842 
R^2.......................: 0.022349 
Adjusted r^2..............: 0.017504 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of FOS.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.022198 
Standard error............: 0.061957 
Odds ratio (effect size)..: 0.978 
Lower 95% CI..............: 0.866 
Upper 95% CI..............: 1.104 
T-value...................: -0.358273 
P-value...................: 0.7202266 
R^2.......................: 0.003983 
Adjusted r^2..............: -0.000628 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.224372 
Standard error............: 0.063739 
Odds ratio (effect size)..: 0.799 
Lower 95% CI..............: 0.705 
Upper 95% CI..............: 0.905 
T-value...................: -3.520195 
P-value...................: 0.0004538281 
R^2.......................: 0.017874 
Adjusted r^2..............: 0.013316 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.035898 
Standard error............: 0.0568 
Odds ratio (effect size)..: 1.037 
Lower 95% CI..............: 0.927 
Upper 95% CI..............: 1.159 
T-value...................: 0.632009 
P-value...................: 0.5275487 
R^2.......................: 0.004211 
Adjusted r^2..............: -0.000415 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.031493 
Standard error............: 0.061581 
Odds ratio (effect size)..: 1.032 
Lower 95% CI..............: 0.915 
Upper 95% CI..............: 1.164 
T-value...................: 0.511398 
P-value...................: 0.6092124 
R^2.......................: 0.006366 
Adjusted r^2..............: 0.001441 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of HMOX1.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.0516 
Standard error............: 0.039919 
Odds ratio (effect size)..: 1.053 
Lower 95% CI..............: 0.974 
Upper 95% CI..............: 1.139 
T-value...................: 1.292619 
P-value...................: 0.1964885 
R^2.......................: 0.010587 
Adjusted r^2..............: 0.006006 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.177408 
Standard error............: 0.040983 
Odds ratio (effect size)..: 0.837 
Lower 95% CI..............: 0.773 
Upper 95% CI..............: 0.907 
T-value...................: -4.328848 
P-value...................: 1.674779e-05 
R^2.......................: 0.029393 
Adjusted r^2..............: 0.024889 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.034321 
Standard error............: 0.036601 
Odds ratio (effect size)..: 1.035 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.112 
T-value...................: 0.937689 
P-value...................: 0.3486674 
R^2.......................: 0.009505 
Adjusted r^2..............: 0.004904 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.054092 
Standard error............: 0.039339 
Odds ratio (effect size)..: 1.056 
Lower 95% CI..............: 0.977 
Upper 95% CI..............: 1.14 
T-value...................: 1.375038 
P-value...................: 0.1695013 
R^2.......................: 0.011117 
Adjusted r^2..............: 0.006216 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of LAPTM5.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.039785 
Standard error............: 0.079616 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.89 
Upper 95% CI..............: 1.216 
T-value...................: 0.499714 
P-value...................: 0.617404 
R^2.......................: 0.003634 
Adjusted r^2..............: -0.000979 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.358584 
Standard error............: 0.0816 
Odds ratio (effect size)..: 0.699 
Lower 95% CI..............: 0.595 
Upper 95% CI..............: 0.82 
T-value...................: -4.394417 
P-value...................: 1.249111e-05 
R^2.......................: 0.025013 
Adjusted r^2..............: 0.020489 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.11374 
Standard error............: 0.072837 
Odds ratio (effect size)..: 1.12 
Lower 95% CI..............: 0.971 
Upper 95% CI..............: 1.292 
T-value...................: 1.561574 
P-value...................: 0.1187559 
R^2.......................: 0.006139 
Adjusted r^2..............: 0.001522 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.034489 
Standard error............: 0.078925 
Odds ratio (effect size)..: 1.035 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.208 
T-value...................: 0.436991 
P-value...................: 0.6622347 
R^2.......................: 0.006782 
Adjusted r^2..............: 0.001859 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of MMP9.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.075837 
Standard error............: 0.074739 
Odds ratio (effect size)..: 1.079 
Lower 95% CI..............: 0.932 
Upper 95% CI..............: 1.249 
T-value...................: 1.014691 
P-value...................: 0.310537 
R^2.......................: 0.003889 
Adjusted r^2..............: -0.000722 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.373842 
Standard error............: 0.076439 
Odds ratio (effect size)..: 0.688 
Lower 95% CI..............: 0.592 
Upper 95% CI..............: 0.799 
T-value...................: -4.89072 
P-value...................: 1.198486e-06 
R^2.......................: 0.029517 
Adjusted r^2..............: 0.025014 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.107832 
Standard error............: 0.068426 
Odds ratio (effect size)..: 1.114 
Lower 95% CI..............: 0.974 
Upper 95% CI..............: 1.274 
T-value...................: 1.575877 
P-value...................: 0.1154215 
R^2.......................: 0.00554 
Adjusted r^2..............: 0.00092 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.063855 
Standard error............: 0.074587 
Odds ratio (effect size)..: 1.066 
Lower 95% CI..............: 0.921 
Upper 95% CI..............: 1.234 
T-value...................: 0.856124 
P-value...................: 0.3921835 
R^2.......................: 0.00292 
Adjusted r^2..............: -0.002023 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of SIGLEC1.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.038867 
Standard error............: 0.067744 
Odds ratio (effect size)..: 1.04 
Lower 95% CI..............: 0.91 
Upper 95% CI..............: 1.187 
T-value...................: 0.573738 
P-value...................: 0.5662943 
R^2.......................: 0.005706 
Adjusted r^2..............: 0.001103 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.278898 
Standard error............: 0.069565 
Odds ratio (effect size)..: 0.757 
Lower 95% CI..............: 0.66 
Upper 95% CI..............: 0.867 
T-value...................: -4.009182 
P-value...................: 6.620613e-05 
R^2.......................: 0.023132 
Adjusted r^2..............: 0.018599 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.095751 
Standard error............: 0.06199 
Odds ratio (effect size)..: 1.1 
Lower 95% CI..............: 0.975 
Upper 95% CI..............: 1.243 
T-value...................: 1.544625 
P-value...................: 0.1228044 
R^2.......................: 0.007836 
Adjusted r^2..............: 0.003227 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.017386 
Standard error............: 0.067424 
Odds ratio (effect size)..: 1.018 
Lower 95% CI..............: 0.892 
Upper 95% CI..............: 1.161 
T-value...................: 0.257869 
P-value...................: 0.7965741 
R^2.......................: 0.007413 
Adjusted r^2..............: 0.002493 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of FTL.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.017409 
Standard error............: 0.079927 
Odds ratio (effect size)..: 1.018 
Lower 95% CI..............: 0.87 
Upper 95% CI..............: 1.19 
T-value...................: 0.217808 
P-value...................: 0.8276302 
R^2.......................: 0.002577 
Adjusted r^2..............: -0.002041 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.317465 
Standard error............: 0.082082 
Odds ratio (effect size)..: 0.728 
Lower 95% CI..............: 0.62 
Upper 95% CI..............: 0.855 
T-value...................: -3.867642 
P-value...................: 0.0001181644 
R^2.......................: 0.019356 
Adjusted r^2..............: 0.014805 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.045428 
Standard error............: 0.073213 
Odds ratio (effect size)..: 1.046 
Lower 95% CI..............: 0.907 
Upper 95% CI..............: 1.208 
T-value...................: 0.620485 
P-value...................: 0.535103 
R^2.......................: 0.002886 
Adjusted r^2..............: -0.001746 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.021477 
Standard error............: 0.078965 
Odds ratio (effect size)..: 1.022 
Lower 95% CI..............: 0.875 
Upper 95% CI..............: 1.193 
T-value...................: 0.271985 
P-value...................: 0.7857029 
R^2.......................: 0.004713 
Adjusted r^2..............: -0.000221 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of CD14.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.045545 
Standard error............: 0.078061 
Odds ratio (effect size)..: 1.047 
Lower 95% CI..............: 0.898 
Upper 95% CI..............: 1.22 
T-value...................: 0.583449 
P-value...................: 0.5597432 
R^2.......................: 0.005132 
Adjusted r^2..............: 0.000526 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.29738 
Standard error............: 0.080274 
Odds ratio (effect size)..: 0.743 
Lower 95% CI..............: 0.635 
Upper 95% CI..............: 0.869 
T-value...................: -3.704578 
P-value...................: 0.0002252345 
R^2.......................: 0.020061 
Adjusted r^2..............: 0.015513 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.066899 
Standard error............: 0.0715 
Odds ratio (effect size)..: 1.069 
Lower 95% CI..............: 0.929 
Upper 95% CI..............: 1.23 
T-value...................: 0.935647 
P-value...................: 0.3497175 
R^2.......................: 0.005661 
Adjusted r^2..............: 0.001041 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.02019 
Standard error............: 0.077651 
Odds ratio (effect size)..: 1.02 
Lower 95% CI..............: 0.876 
Upper 95% CI..............: 1.188 
T-value...................: 0.26001 
P-value...................: 0.7949226 
R^2.......................: 0.007596 
Adjusted r^2..............: 0.002677 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of HCST.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.035916 
Standard error............: 0.077071 
Odds ratio (effect size)..: 1.037 
Lower 95% CI..............: 0.891 
Upper 95% CI..............: 1.206 
T-value...................: 0.466018 
P-value...................: 0.6413197 
R^2.......................: 0.005482 
Adjusted r^2..............: 0.000878 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.290021 
Standard error............: 0.079316 
Odds ratio (effect size)..: 0.748 
Lower 95% CI..............: 0.641 
Upper 95% CI..............: 0.874 
T-value...................: -3.656518 
P-value...................: 0.0002711566 
R^2.......................: 0.019982 
Adjusted r^2..............: 0.015434 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.09001 
Standard error............: 0.070542 
Odds ratio (effect size)..: 1.094 
Lower 95% CI..............: 0.953 
Upper 95% CI..............: 1.256 
T-value...................: 1.275971 
P-value...................: 0.2023098 
R^2.......................: 0.006909 
Adjusted r^2..............: 0.002295 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.038898 
Standard error............: 0.076466 
Odds ratio (effect size)..: 1.04 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.208 
T-value...................: 0.508695 
P-value...................: 0.6111051 
R^2.......................: 0.010011 
Adjusted r^2..............: 0.005103 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of TIMP3.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.059325 
Standard error............: 0.067665 
Odds ratio (effect size)..: 1.061 
Lower 95% CI..............: 0.929 
Upper 95% CI..............: 1.212 
T-value...................: 0.876739 
P-value...................: 0.380872 
R^2.......................: 0.010615 
Adjusted r^2..............: 0.006035 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.18139 
Standard error............: 0.069842 
Odds ratio (effect size)..: 0.834 
Lower 95% CI..............: 0.727 
Upper 95% CI..............: 0.956 
T-value...................: -2.59714 
P-value...................: 0.009560593 
R^2.......................: 0.017046 
Adjusted r^2..............: 0.012485 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.029893 
Standard error............: 0.062044 
Odds ratio (effect size)..: 0.971 
Lower 95% CI..............: 0.859 
Upper 95% CI..............: 1.096 
T-value...................: -0.481812 
P-value...................: 0.6300619 
R^2.......................: 0.009624 
Adjusted r^2..............: 0.005022 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.025315 
Standard error............: 0.067455 
Odds ratio (effect size)..: 0.975 
Lower 95% CI..............: 0.854 
Upper 95% CI..............: 1.113 
T-value...................: -0.375294 
P-value...................: 0.7075402 
R^2.......................: 0.010592 
Adjusted r^2..............: 0.005688 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of CCL2.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.044765 
Standard error............: 0.064119 
Odds ratio (effect size)..: 0.956 
Lower 95% CI..............: 0.843 
Upper 95% CI..............: 1.084 
T-value...................: -0.698156 
P-value...................: 0.4852674 
R^2.......................: 0.006252 
Adjusted r^2..............: 0.001652 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.250689 
Standard error............: 0.065902 
Odds ratio (effect size)..: 0.778 
Lower 95% CI..............: 0.684 
Upper 95% CI..............: 0.886 
T-value...................: -3.803974 
P-value...................: 0.0001524425 
R^2.......................: 0.022027 
Adjusted r^2..............: 0.017489 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.002588 
Standard error............: 0.058802 
Odds ratio (effect size)..: 1.003 
Lower 95% CI..............: 0.893 
Upper 95% CI..............: 1.125 
T-value...................: 0.044013 
P-value...................: 0.9649039 
R^2.......................: 0.00567 
Adjusted r^2..............: 0.001051 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.024892 
Standard error............: 0.063558 
Odds ratio (effect size)..: 1.025 
Lower 95% CI..............: 0.905 
Upper 95% CI..............: 1.161 
T-value...................: 0.391646 
P-value...................: 0.6954231 
R^2.......................: 0.008408 
Adjusted r^2..............: 0.003494 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of SAT1.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.00292 
Standard error............: 0.076252 
Odds ratio (effect size)..: 1.003 
Lower 95% CI..............: 0.864 
Upper 95% CI..............: 1.165 
T-value...................: 0.038298 
P-value...................: 0.9694591 
R^2.......................: 0.00453 
Adjusted r^2..............: -7.8e-05 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.330388 
Standard error............: 0.078139 
Odds ratio (effect size)..: 0.719 
Lower 95% CI..............: 0.617 
Upper 95% CI..............: 0.838 
T-value...................: -4.228208 
P-value...................: 2.607247e-05 
R^2.......................: 0.024558 
Adjusted r^2..............: 0.020032 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.026399 
Standard error............: 0.069821 
Odds ratio (effect size)..: 1.027 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.177 
T-value...................: 0.378096 
P-value...................: 0.7054521 
R^2.......................: 0.004634 
Adjusted r^2..............: 9e-06 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.021537 
Standard error............: 0.075705 
Odds ratio (effect size)..: 1.022 
Lower 95% CI..............: 0.881 
Upper 95% CI..............: 1.185 
T-value...................: 0.284489 
P-value...................: 0.7761083 
R^2.......................: 0.006378 
Adjusted r^2..............: 0.001453 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of CD163.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.00116 
Standard error............: 0.049738 
Odds ratio (effect size)..: 1.001 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.104 
T-value...................: 0.02332 
P-value...................: 0.9814008 
R^2.......................: 0.002808 
Adjusted r^2..............: -0.001809 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.193213 
Standard error............: 0.05114 
Odds ratio (effect size)..: 0.824 
Lower 95% CI..............: 0.746 
Upper 95% CI..............: 0.911 
T-value...................: -3.778142 
P-value...................: 0.0001688635 
R^2.......................: 0.018994 
Adjusted r^2..............: 0.014442 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.016507 
Standard error............: 0.045602 
Odds ratio (effect size)..: 1.017 
Lower 95% CI..............: 0.93 
Upper 95% CI..............: 1.112 
T-value...................: 0.361991 
P-value...................: 0.7174474 
R^2.......................: 0.003004 
Adjusted r^2..............: -0.001628 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.020021 
Standard error............: 0.04938 
Odds ratio (effect size)..: 1.02 
Lower 95% CI..............: 0.926 
Upper 95% CI..............: 1.124 
T-value...................: 0.405446 
P-value...................: 0.6852572 
R^2.......................: 0.003732 
Adjusted r^2..............: -0.001206 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of PTGDS.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.07203 
Standard error............: 0.058021 
Odds ratio (effect size)..: 1.075 
Lower 95% CI..............: 0.959 
Upper 95% CI..............: 1.204 
T-value...................: 1.241435 
P-value...................: 0.2147821 
R^2.......................: 0.005419 
Adjusted r^2..............: 0.000815 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.154708 
Standard error............: 0.059964 
Odds ratio (effect size)..: 0.857 
Lower 95% CI..............: 0.762 
Upper 95% CI..............: 0.964 
T-value...................: -2.580002 
P-value...................: 0.01004443 
R^2.......................: 0.011181 
Adjusted r^2..............: 0.006592 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.00745 
Standard error............: 0.053258 
Odds ratio (effect size)..: 1.007 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.118 
T-value...................: 0.139886 
P-value...................: 0.8887831 
R^2.......................: 0.003636 
Adjusted r^2..............: -0.000992 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.076181 
Standard error............: 0.058227 
Odds ratio (effect size)..: 1.079 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.21 
T-value...................: 1.308344 
P-value...................: 0.1911292 
R^2.......................: 0.007067 
Adjusted r^2..............: 0.002145 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of LGALS9.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.036416 
Standard error............: 0.064227 
Odds ratio (effect size)..: 1.037 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.176 
T-value...................: 0.566985 
P-value...................: 0.5708718 
R^2.......................: 0.004688 
Adjusted r^2..............: 8e-05 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.255799 
Standard error............: 0.065956 
Odds ratio (effect size)..: 0.774 
Lower 95% CI..............: 0.68 
Upper 95% CI..............: 0.881 
T-value...................: -3.878321 
P-value...................: 0.000113182 
R^2.......................: 0.021057 
Adjusted r^2..............: 0.016515 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.054661 
Standard error............: 0.058801 
Odds ratio (effect size)..: 1.056 
Lower 95% CI..............: 0.941 
Upper 95% CI..............: 1.185 
T-value...................: 0.929602 
P-value...................: 0.352838 
R^2.......................: 0.005129 
Adjusted r^2..............: 0.000507 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.047972 
Standard error............: 0.063624 
Odds ratio (effect size)..: 1.049 
Lower 95% CI..............: 0.926 
Upper 95% CI..............: 1.188 
T-value...................: 0.753995 
P-value...................: 0.451072 
R^2.......................: 0.008663 
Adjusted r^2..............: 0.003749 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of ACKR1.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.033286 
Standard error............: 0.053746 
Odds ratio (effect size)..: 1.034 
Lower 95% CI..............: 0.93 
Upper 95% CI..............: 1.149 
T-value...................: 0.619333 
P-value...................: 0.5358605 
R^2.......................: 0.007231 
Adjusted r^2..............: 0.002635 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.200006 
Standard error............: 0.055312 
Odds ratio (effect size)..: 0.819 
Lower 95% CI..............: 0.735 
Upper 95% CI..............: 0.912 
T-value...................: -3.616001 
P-value...................: 0.0003165611 
R^2.......................: 0.021395 
Adjusted r^2..............: 0.016854 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.001823 
Standard error............: 0.049311 
Odds ratio (effect size)..: 1.002 
Lower 95% CI..............: 0.91 
Upper 95% CI..............: 1.103 
T-value...................: 0.036969 
P-value...................: 0.9705185 
R^2.......................: 0.006634 
Adjusted r^2..............: 0.002019 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.10824 
Standard error............: 0.05401 
Odds ratio (effect size)..: 1.114 
Lower 95% CI..............: 1.002 
Upper 95% CI..............: 1.239 
T-value...................: 2.004077 
P-value...................: 0.04539552 
R^2.......................: 0.014288 
Adjusted r^2..............: 0.009402 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of NT5DC2.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.064431 
Standard error............: 0.057148 
Odds ratio (effect size)..: 0.938 
Lower 95% CI..............: 0.838 
Upper 95% CI..............: 1.049 
T-value...................: -1.127441 
P-value...................: 0.259869 
R^2.......................: 0.003654 
Adjusted r^2..............: -0.000958 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.198497 
Standard error............: 0.05887 
Odds ratio (effect size)..: 0.82 
Lower 95% CI..............: 0.731 
Upper 95% CI..............: 0.92 
T-value...................: -3.371776 
P-value...................: 0.0007800922 
R^2.......................: 0.015059 
Adjusted r^2..............: 0.010488 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.004794 
Standard error............: 0.05244 
Odds ratio (effect size)..: 1.005 
Lower 95% CI..............: 0.907 
Upper 95% CI..............: 1.114 
T-value...................: 0.091424 
P-value...................: 0.9271768 
R^2.......................: 0.002099 
Adjusted r^2..............: -0.002537 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.003965 
Standard error............: 0.05687 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.898 
Upper 95% CI..............: 1.122 
T-value...................: 0.069714 
P-value...................: 0.9444388 
R^2.......................: 0.002624 
Adjusted r^2..............: -0.00232 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of TGFBI.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.030072 
Standard error............: 0.063823 
Odds ratio (effect size)..: 0.97 
Lower 95% CI..............: 0.856 
Upper 95% CI..............: 1.1 
T-value...................: -0.471178 
P-value...................: 0.6376329 
R^2.......................: 0.009649 
Adjusted r^2..............: 0.005064 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.226791 
Standard error............: 0.065665 
Odds ratio (effect size)..: 0.797 
Lower 95% CI..............: 0.701 
Upper 95% CI..............: 0.907 
T-value...................: -3.453775 
P-value...................: 0.0005797429 
R^2.......................: 0.022856 
Adjusted r^2..............: 0.018322 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.005525 
Standard error............: 0.058508 
Odds ratio (effect size)..: 1.006 
Lower 95% CI..............: 0.897 
Upper 95% CI..............: 1.128 
T-value...................: 0.094441 
P-value...................: 0.9247811 
R^2.......................: 0.009372 
Adjusted r^2..............: 0.00477 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.002111 
Standard error............: 0.063453 
Odds ratio (effect size)..: 1.002 
Lower 95% CI..............: 0.885 
Upper 95% CI..............: 1.135 
T-value...................: 0.033269 
P-value...................: 0.9734681 
R^2.......................: 0.008829 
Adjusted r^2..............: 0.003917 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of C1QC.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.025493 
Standard error............: 0.069697 
Odds ratio (effect size)..: 1.026 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.176 
T-value...................: 0.365774 
P-value...................: 0.7146229 
R^2.......................: 0.008583 
Adjusted r^2..............: 0.003993 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.283741 
Standard error............: 0.07159 
Odds ratio (effect size)..: 0.753 
Lower 95% CI..............: 0.654 
Upper 95% CI..............: 0.866 
T-value...................: -3.963406 
P-value...................: 8.00056e-05 
R^2.......................: 0.025834 
Adjusted r^2..............: 0.021313 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.077613 
Standard error............: 0.063823 
Odds ratio (effect size)..: 1.081 
Lower 95% CI..............: 0.954 
Upper 95% CI..............: 1.225 
T-value...................: 1.216073 
P-value...................: 0.2242903 
R^2.......................: 0.009949 
Adjusted r^2..............: 0.005349 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.009869 
Standard error............: 0.069375 
Odds ratio (effect size)..: 1.01 
Lower 95% CI..............: 0.882 
Upper 95% CI..............: 1.157 
T-value...................: 0.142258 
P-value...................: 0.8869121 
R^2.......................: 0.012561 
Adjusted r^2..............: 0.007667 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

Analysis of S100A9.

- processing MAC_rankNorm

filter: removed 223 rows (20%), 869 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.005659 
Standard error............: 0.051608 
Odds ratio (effect size)..: 0.994 
Lower 95% CI..............: 0.899 
Upper 95% CI..............: 1.1 
T-value...................: -0.109644 
P-value...................: 0.9127172 
R^2.......................: 0.004002 
Adjusted r^2..............: -0.000609 
Sample size of AE DB......: 1092 
Sample size of model......: 869 
Missing data %............: 20.42124 

- processing SMC_rankNorm

filter: removed 225 rows (21%), 867 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.228809 
Standard error............: 0.052912 
Odds ratio (effect size)..: 0.795 
Lower 95% CI..............: 0.717 
Upper 95% CI..............: 0.882 
T-value...................: -4.324337 
P-value...................: 1.708669e-05 
R^2.......................: 0.024902 
Adjusted r^2..............: 0.020377 
Sample size of AE DB......: 1092 
Sample size of model......: 867 
Missing data %............: 20.6044 

- processing MAC_SMC_ratio_rank

filter: removed 226 rows (21%), 866 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.037964 
Standard error............: 0.047251 
Odds ratio (effect size)..: 1.039 
Lower 95% CI..............: 0.947 
Upper 95% CI..............: 1.139 
T-value...................: 0.803453 
P-value...................: 0.4219345 
R^2.......................: 0.004687 
Adjusted r^2..............: 6.3e-05 
Sample size of AE DB......: 1092 
Sample size of model......: 866 
Missing data %............: 20.69597 

- processing VesselDensity_rankNorm

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.035344 
Standard error............: 0.051349 
Odds ratio (effect size)..: 1.036 
Lower 95% CI..............: 0.937 
Upper 95% CI..............: 1.146 
T-value...................: 0.688305 
P-value...................: 0.4914583 
R^2.......................: 0.005005 
Adjusted r^2..............: 7.3e-05 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103

cat("Edit the column names...\n")

Edit the column names...

colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")

Correct the variable types...

GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

Writing results to Excel-file...

### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Con.Uni.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")

Removing intermediate files...

rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of plaque CONVOCALS_downstream expression levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year,
    #           data  =  currentDF, family = binomial(link = "logit"))
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch,
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    # print(model_step)
    # print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}


Analysis of SCGB3A2.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.060976 
Standard error............: 0.056214 
Odds ratio (effect size)..: 0.941 
Lower 95% CI..............: 0.843 
Upper 95% CI..............: 1.05 
Z-value...................: -1.0847 
P-value...................: 0.2780547 
Hosmer and Lemeshow r^2...: 0.090537 
Cox and Snell r^2.........: 0.117842 
Nagelkerke's pseudo r^2...: 0.157196 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.014859 
Standard error............: 0.066871 
Odds ratio (effect size)..: 1.015 
Lower 95% CI..............: 0.89 
Upper 95% CI..............: 1.157 
Z-value...................: 0.222209 
P-value...................: 0.8241514 
Hosmer and Lemeshow r^2...: 0.004662 
Cox and Snell r^2.........: 0.004731 
Nagelkerke's pseudo r^2...: 0.007411 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.282835 
Standard error............: 0.06191 
Odds ratio (effect size)..: 0.754 
Lower 95% CI..............: 0.668 
Upper 95% CI..............: 0.851 
Z-value...................: -4.568451 
P-value...................: 4.913414e-06 
Hosmer and Lemeshow r^2...: 0.067559 
Cox and Snell r^2.........: 0.073806 
Nagelkerke's pseudo r^2...: 0.108771 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: IPH 
Effect size...............: -0.094332 
Standard error............: 0.055804 
Odds ratio (effect size)..: 0.91 
Lower 95% CI..............: 0.816 
Upper 95% CI..............: 1.015 
Z-value...................: -1.690432 
P-value...................: 0.09094541 
Hosmer and Lemeshow r^2...: 0.034883 
Cox and Snell r^2.........: 0.045762 
Nagelkerke's pseudo r^2...: 0.061934 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.002153 
Standard error............: 0.05538 
Odds ratio (effect size)..: 1.002 
Lower 95% CI..............: 0.899 
Upper 95% CI..............: 1.117 
Z-value...................: 0.038884 
P-value...................: 0.9689828 
Hosmer and Lemeshow r^2...: 0.02326 
Cox and Snell r^2.........: 0.031406 
Nagelkerke's pseudo r^2...: 0.042079 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.164514 
Standard error............: 0.059905 
Odds ratio (effect size)..: 1.179 
Lower 95% CI..............: 1.048 
Upper 95% CI..............: 1.326 
Z-value...................: 2.746263 
P-value...................: 0.006027838 
Hosmer and Lemeshow r^2...: 0.025596 
Cox and Snell r^2.........: 0.032035 
Nagelkerke's pseudo r^2...: 0.044509 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of LIX1.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.033649 
Standard error............: 0.075266 
Odds ratio (effect size)..: 0.967 
Lower 95% CI..............: 0.834 
Upper 95% CI..............: 1.121 
Z-value...................: -0.447073 
P-value...................: 0.6548224 
Hosmer and Lemeshow r^2...: 0.089801 
Cox and Snell r^2.........: 0.116942 
Nagelkerke's pseudo r^2...: 0.155996 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.172085 
Standard error............: 0.088799 
Odds ratio (effect size)..: 1.188 
Lower 95% CI..............: 0.998 
Upper 95% CI..............: 1.414 
Z-value...................: 1.937902 
P-value...................: 0.05263517 
Hosmer and Lemeshow r^2...: 0.008603 
Cox and Snell r^2.........: 0.008713 
Nagelkerke's pseudo r^2...: 0.013648 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.592081 
Standard error............: 0.0883 
Odds ratio (effect size)..: 0.553 
Lower 95% CI..............: 0.465 
Upper 95% CI..............: 0.658 
Z-value...................: -6.705349 
P-value...................: 2.009259e-11 
Hosmer and Lemeshow r^2...: 0.092793 
Cox and Snell r^2.........: 0.099953 
Nagelkerke's pseudo r^2...: 0.147306 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: IPH 
Effect size...............: -0.28004 
Standard error............: 0.075776 
Odds ratio (effect size)..: 0.756 
Lower 95% CI..............: 0.651 
Upper 95% CI..............: 0.877 
Z-value...................: -3.695656 
P-value...................: 0.0002193199 
Hosmer and Lemeshow r^2...: 0.043706 
Cox and Snell r^2.........: 0.057001 
Nagelkerke's pseudo r^2...: 0.077143 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.296724 
Standard error............: 0.07516 
Odds ratio (effect size)..: 0.743 
Lower 95% CI..............: 0.641 
Upper 95% CI..............: 0.861 
Z-value...................: -3.947885 
P-value...................: 7.884455e-05 
Hosmer and Lemeshow r^2...: 0.035818 
Cox and Snell r^2.........: 0.047949 
Nagelkerke's pseudo r^2...: 0.064244 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.350429 
Standard error............: 0.079028 
Odds ratio (effect size)..: 1.42 
Lower 95% CI..............: 1.216 
Upper 95% CI..............: 1.658 
Z-value...................: 4.434213 
P-value...................: 9.240925e-06 
Hosmer and Lemeshow r^2...: 0.036209 
Cox and Snell r^2.........: 0.045014 
Nagelkerke's pseudo r^2...: 0.062542 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of IGSF9B.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.017009 
Standard error............: 0.05711 
Odds ratio (effect size)..: 0.983 
Lower 95% CI..............: 0.879 
Upper 95% CI..............: 1.1 
Z-value...................: -0.297828 
P-value...................: 0.7658342 
Hosmer and Lemeshow r^2...: 0.089717 
Cox and Snell r^2.........: 0.116839 
Nagelkerke's pseudo r^2...: 0.155859 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.01389 
Standard error............: 0.067663 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.864 
Upper 95% CI..............: 1.126 
Z-value...................: -0.205279 
P-value...................: 0.8373542 
Hosmer and Lemeshow r^2...: 0.004654 
Cox and Snell r^2.........: 0.004723 
Nagelkerke's pseudo r^2...: 0.007398 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.209493 
Standard error............: 0.0626 
Odds ratio (effect size)..: 0.811 
Lower 95% CI..............: 0.717 
Upper 95% CI..............: 0.917 
Z-value...................: -3.346531 
P-value...................: 0.0008182939 
Hosmer and Lemeshow r^2...: 0.058516 
Cox and Snell r^2.........: 0.064251 
Nagelkerke's pseudo r^2...: 0.094691 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: IPH 
Effect size...............: -0.050611 
Standard error............: 0.056754 
Odds ratio (effect size)..: 0.951 
Lower 95% CI..............: 0.851 
Upper 95% CI..............: 1.063 
Z-value...................: -0.891763 
P-value...................: 0.37252 
Hosmer and Lemeshow r^2...: 0.03324 
Cox and Snell r^2.........: 0.043654 
Nagelkerke's pseudo r^2...: 0.059081 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.058101 
Standard error............: 0.055958 
Odds ratio (effect size)..: 0.944 
Lower 95% CI..............: 0.846 
Upper 95% CI..............: 1.053 
Z-value...................: -1.0383 
P-value...................: 0.2991306 
Hosmer and Lemeshow r^2...: 0.024108 
Cox and Snell r^2.........: 0.032532 
Nagelkerke's pseudo r^2...: 0.043587 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.131452 
Standard error............: 0.060514 
Odds ratio (effect size)..: 1.14 
Lower 95% CI..............: 1.013 
Upper 95% CI..............: 1.284 
Z-value...................: 2.172244 
P-value...................: 0.02983724 
Hosmer and Lemeshow r^2...: 0.02309 
Cox and Snell r^2.........: 0.028944 
Nagelkerke's pseudo r^2...: 0.040215 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of ALB.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.098463 
Standard error............: 0.070056 
Odds ratio (effect size)..: 0.906 
Lower 95% CI..............: 0.79 
Upper 95% CI..............: 1.04 
Z-value...................: -1.405479 
P-value...................: 0.1598789 
Hosmer and Lemeshow r^2...: 0.091151 
Cox and Snell r^2.........: 0.118591 
Nagelkerke's pseudo r^2...: 0.158196 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.052742 
Standard error............: 0.081949 
Odds ratio (effect size)..: 1.054 
Lower 95% CI..............: 0.898 
Upper 95% CI..............: 1.238 
Z-value...................: 0.643592 
P-value...................: 0.5198399 
Hosmer and Lemeshow r^2...: 0.005046 
Cox and Snell r^2.........: 0.005119 
Nagelkerke's pseudo r^2...: 0.008019 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.475677 
Standard error............: 0.084404 
Odds ratio (effect size)..: 0.621 
Lower 95% CI..............: 0.527 
Upper 95% CI..............: 0.733 
Z-value...................: -5.63571 
P-value...................: 1.743385e-08 
Hosmer and Lemeshow r^2...: 0.079741 
Cox and Snell r^2.........: 0.086522 
Nagelkerke's pseudo r^2...: 0.127512 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: IPH 
Effect size...............: -0.203012 
Standard error............: 0.070944 
Odds ratio (effect size)..: 0.816 
Lower 95% CI..............: 0.71 
Upper 95% CI..............: 0.938 
Z-value...................: -2.861587 
P-value...................: 0.004215256 
Hosmer and Lemeshow r^2...: 0.03928 
Cox and Snell r^2.........: 0.05138 
Nagelkerke's pseudo r^2...: 0.069536 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.244373 
Standard error............: 0.069788 
Odds ratio (effect size)..: 0.783 
Lower 95% CI..............: 0.683 
Upper 95% CI..............: 0.898 
Z-value...................: -3.501664 
P-value...................: 0.0004623625 
Hosmer and Lemeshow r^2...: 0.033201 
Cox and Snell r^2.........: 0.044526 
Nagelkerke's pseudo r^2...: 0.059657 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.110518 
Standard error............: 0.07054 
Odds ratio (effect size)..: 1.117 
Lower 95% CI..............: 0.973 
Upper 95% CI..............: 1.282 
Z-value...................: 1.566745 
P-value...................: 0.1171743 
Hosmer and Lemeshow r^2...: 0.021077 
Cox and Snell r^2.........: 0.026454 
Nagelkerke's pseudo r^2...: 0.036756 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of OR10A4.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.066354 
Standard error............: 0.06469 
Odds ratio (effect size)..: 0.936 
Lower 95% CI..............: 0.824 
Upper 95% CI..............: 1.062 
Z-value...................: -1.025724 
P-value...................: 0.3050217 
Hosmer and Lemeshow r^2...: 0.090447 
Cox and Snell r^2.........: 0.117731 
Nagelkerke's pseudo r^2...: 0.157049 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.06206 
Standard error............: 0.075486 
Odds ratio (effect size)..: 1.064 
Lower 95% CI..............: 0.918 
Upper 95% CI..............: 1.234 
Z-value...................: 0.822138 
P-value...................: 0.4109985 
Hosmer and Lemeshow r^2...: 0.00532 
Cox and Snell r^2.........: 0.005398 
Nagelkerke's pseudo r^2...: 0.008455 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.46289 
Standard error............: 0.078261 
Odds ratio (effect size)..: 0.629 
Lower 95% CI..............: 0.54 
Upper 95% CI..............: 0.734 
Z-value...................: -5.914686 
P-value...................: 3.325095e-09 
Hosmer and Lemeshow r^2...: 0.08312 
Cox and Snell r^2.........: 0.090019 
Nagelkerke's pseudo r^2...: 0.132666 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: IPH 
Effect size...............: -0.186198 
Standard error............: 0.065422 
Odds ratio (effect size)..: 0.83 
Lower 95% CI..............: 0.73 
Upper 95% CI..............: 0.944 
Z-value...................: -2.846126 
P-value...................: 0.004425465 
Hosmer and Lemeshow r^2...: 0.0392 
Cox and Snell r^2.........: 0.051278 
Nagelkerke's pseudo r^2...: 0.069398 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.191658 
Standard error............: 0.064343 
Odds ratio (effect size)..: 0.826 
Lower 95% CI..............: 0.728 
Upper 95% CI..............: 0.937 
Z-value...................: -2.978691 
P-value...................: 0.002894829 
Hosmer and Lemeshow r^2...: 0.030396 
Cox and Snell r^2.........: 0.040841 
Nagelkerke's pseudo r^2...: 0.05472 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.13207 
Standard error............: 0.065428 
Odds ratio (effect size)..: 1.141 
Lower 95% CI..............: 1.004 
Upper 95% CI..............: 1.297 
Z-value...................: 2.018537 
P-value...................: 0.04353534 
Hosmer and Lemeshow r^2...: 0.022457 
Cox and Snell r^2.........: 0.028161 
Nagelkerke's pseudo r^2...: 0.039127 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of RASEF.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.041214 
Standard error............: 0.069104 
Odds ratio (effect size)..: 0.96 
Lower 95% CI..............: 0.838 
Upper 95% CI..............: 1.099 
Z-value...................: -0.596404 
P-value...................: 0.5509052 
Hosmer and Lemeshow r^2...: 0.089919 
Cox and Snell r^2.........: 0.117086 
Nagelkerke's pseudo r^2...: 0.156188 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.059181 
Standard error............: 0.082383 
Odds ratio (effect size)..: 0.943 
Lower 95% CI..............: 0.802 
Upper 95% CI..............: 1.108 
Z-value...................: -0.718366 
P-value...................: 0.4725314 
Hosmer and Lemeshow r^2...: 0.005158 
Cox and Snell r^2.........: 0.005233 
Nagelkerke's pseudo r^2...: 0.008197 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.566007 
Standard error............: 0.088969 
Odds ratio (effect size)..: 0.568 
Lower 95% CI..............: 0.477 
Upper 95% CI..............: 0.676 
Z-value...................: -6.361873 
P-value...................: 1.993077e-10 
Hosmer and Lemeshow r^2...: 0.089454 
Cox and Snell r^2.........: 0.096536 
Nagelkerke's pseudo r^2...: 0.14227 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: IPH 
Effect size...............: -0.132295 
Standard error............: 0.070078 
Odds ratio (effect size)..: 0.876 
Lower 95% CI..............: 0.764 
Upper 95% CI..............: 1.005 
Z-value...................: -1.887816 
P-value...................: 0.05905066 
Hosmer and Lemeshow r^2...: 0.035494 
Cox and Snell r^2.........: 0.046544 
Nagelkerke's pseudo r^2...: 0.062991 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.104899 
Standard error............: 0.06823 
Odds ratio (effect size)..: 0.9 
Lower 95% CI..............: 0.788 
Upper 95% CI..............: 1.029 
Z-value...................: -1.537435 
P-value...................: 0.1241869 
Hosmer and Lemeshow r^2...: 0.025141 
Cox and Snell r^2.........: 0.033902 
Nagelkerke's pseudo r^2...: 0.045423 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.111816 
Standard error............: 0.070037 
Odds ratio (effect size)..: 1.118 
Lower 95% CI..............: 0.975 
Upper 95% CI..............: 1.283 
Z-value...................: 1.596527 
P-value...................: 0.1103712 
Hosmer and Lemeshow r^2...: 0.021154 
Cox and Snell r^2.........: 0.02655 
Nagelkerke's pseudo r^2...: 0.036888 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of NEDD4.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.058379 
Standard error............: 0.072513 
Odds ratio (effect size)..: 0.943 
Lower 95% CI..............: 0.818 
Upper 95% CI..............: 1.087 
Z-value...................: -0.805088 
P-value...................: 0.4207688 
Hosmer and Lemeshow r^2...: 0.09014 
Cox and Snell r^2.........: 0.117357 
Nagelkerke's pseudo r^2...: 0.156549 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.031623 
Standard error............: 0.085125 
Odds ratio (effect size)..: 1.032 
Lower 95% CI..............: 0.874 
Upper 95% CI..............: 1.22 
Z-value...................: 0.371494 
P-value...................: 0.7102699 
Hosmer and Lemeshow r^2...: 0.004755 
Cox and Snell r^2.........: 0.004825 
Nagelkerke's pseudo r^2...: 0.007558 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.539083 
Standard error............: 0.091295 
Odds ratio (effect size)..: 0.583 
Lower 95% CI..............: 0.488 
Upper 95% CI..............: 0.698 
Z-value...................: -5.904862 
P-value...................: 3.529431e-09 
Hosmer and Lemeshow r^2...: 0.083371 
Cox and Snell r^2.........: 0.090277 
Nagelkerke's pseudo r^2...: 0.133047 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: IPH 
Effect size...............: -0.174262 
Standard error............: 0.073548 
Odds ratio (effect size)..: 0.84 
Lower 95% CI..............: 0.727 
Upper 95% CI..............: 0.97 
Z-value...................: -2.36936 
P-value...................: 0.01781891 
Hosmer and Lemeshow r^2...: 0.037164 
Cox and Snell r^2.........: 0.048681 
Nagelkerke's pseudo r^2...: 0.065883 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.160517 
Standard error............: 0.071803 
Odds ratio (effect size)..: 0.852 
Lower 95% CI..............: 0.74 
Upper 95% CI..............: 0.98 
Z-value...................: -2.235535 
P-value...................: 0.02538222 
Hosmer and Lemeshow r^2...: 0.027253 
Cox and Snell r^2.........: 0.036698 
Nagelkerke's pseudo r^2...: 0.049168 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.100734 
Standard error............: 0.073252 
Odds ratio (effect size)..: 1.106 
Lower 95% CI..............: 0.958 
Upper 95% CI..............: 1.277 
Z-value...................: 1.375186 
P-value...................: 0.1690739 
Hosmer and Lemeshow r^2...: 0.020597 
Cox and Snell r^2.........: 0.02586 
Nagelkerke's pseudo r^2...: 0.035929 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of TCL1A.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.011426 
Standard error............: 0.082362 
Odds ratio (effect size)..: 0.989 
Lower 95% CI..............: 0.841 
Upper 95% CI..............: 1.162 
Z-value...................: -0.138726 
P-value...................: 0.8896667 
Hosmer and Lemeshow r^2...: 0.089665 
Cox and Snell r^2.........: 0.116775 
Nagelkerke's pseudo r^2...: 0.155773 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.232564 
Standard error............: 0.09822 
Odds ratio (effect size)..: 1.262 
Lower 95% CI..............: 1.041 
Upper 95% CI..............: 1.53 
Z-value...................: 2.367777 
P-value...................: 0.01789531 
Hosmer and Lemeshow r^2...: 0.010602 
Cox and Snell r^2.........: 0.010727 
Nagelkerke's pseudo r^2...: 0.016802 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.642105 
Standard error............: 0.09678 
Odds ratio (effect size)..: 0.526 
Lower 95% CI..............: 0.435 
Upper 95% CI..............: 0.636 
Z-value...................: -6.634708 
P-value...................: 3.251455e-11 
Hosmer and Lemeshow r^2...: 0.091665 
Cox and Snell r^2.........: 0.0988 
Nagelkerke's pseudo r^2...: 0.145607 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: IPH 
Effect size...............: -0.306825 
Standard error............: 0.083076 
Odds ratio (effect size)..: 0.736 
Lower 95% CI..............: 0.625 
Upper 95% CI..............: 0.866 
Z-value...................: -3.693304 
P-value...................: 0.000221359 
Hosmer and Lemeshow r^2...: 0.043687 
Cox and Snell r^2.........: 0.056977 
Nagelkerke's pseudo r^2...: 0.077111 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.305656 
Standard error............: 0.082036 
Odds ratio (effect size)..: 0.737 
Lower 95% CI..............: 0.627 
Upper 95% CI..............: 0.865 
Z-value...................: -3.72589 
P-value...................: 0.000194627 
Hosmer and Lemeshow r^2...: 0.034416 
Cox and Snell r^2.........: 0.046116 
Nagelkerke's pseudo r^2...: 0.061787 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.414638 
Standard error............: 0.087418 
Odds ratio (effect size)..: 1.514 
Lower 95% CI..............: 1.275 
Upper 95% CI..............: 1.797 
Z-value...................: 4.743157 
P-value...................: 2.104126e-06 
Hosmer and Lemeshow r^2...: 0.038834 
Cox and Snell r^2.........: 0.048198 
Nagelkerke's pseudo r^2...: 0.066966 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of FBXO15.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.098527 
Standard error............: 0.072481 
Odds ratio (effect size)..: 0.906 
Lower 95% CI..............: 0.786 
Upper 95% CI..............: 1.044 
Z-value...................: -1.359352 
P-value...................: 0.1740349 
Hosmer and Lemeshow r^2...: 0.091055 
Cox and Snell r^2.........: 0.118474 
Nagelkerke's pseudo r^2...: 0.158039 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.020066 
Standard error............: 0.085566 
Odds ratio (effect size)..: 0.98 
Lower 95% CI..............: 0.829 
Upper 95% CI..............: 1.159 
Z-value...................: -0.234509 
P-value...................: 0.81459 
Hosmer and Lemeshow r^2...: 0.004668 
Cox and Snell r^2.........: 0.004737 
Nagelkerke's pseudo r^2...: 0.00742 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.553272 
Standard error............: 0.093525 
Odds ratio (effect size)..: 0.575 
Lower 95% CI..............: 0.479 
Upper 95% CI..............: 0.691 
Z-value...................: -5.915782 
P-value...................: 3.303019e-09 
Hosmer and Lemeshow r^2...: 0.084283 
Cox and Snell r^2.........: 0.091218 
Nagelkerke's pseudo r^2...: 0.134433 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: IPH 
Effect size...............: -0.166016 
Standard error............: 0.073571 
Odds ratio (effect size)..: 0.847 
Lower 95% CI..............: 0.733 
Upper 95% CI..............: 0.978 
Z-value...................: -2.256531 
P-value...................: 0.02403741 
Hosmer and Lemeshow r^2...: 0.036751 
Cox and Snell r^2.........: 0.048152 
Nagelkerke's pseudo r^2...: 0.065168 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.199843 
Standard error............: 0.072244 
Odds ratio (effect size)..: 0.819 
Lower 95% CI..............: 0.711 
Upper 95% CI..............: 0.943 
Z-value...................: -2.766236 
P-value...................: 0.00567075 
Hosmer and Lemeshow r^2...: 0.029433 
Cox and Snell r^2.........: 0.039573 
Nagelkerke's pseudo r^2...: 0.053021 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.129649 
Standard error............: 0.072609 
Odds ratio (effect size)..: 1.138 
Lower 95% CI..............: 0.987 
Upper 95% CI..............: 1.313 
Z-value...................: 1.785576 
P-value...................: 0.07416788 
Hosmer and Lemeshow r^2...: 0.021691 
Cox and Snell r^2.........: 0.027214 
Nagelkerke's pseudo r^2...: 0.037811 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of F5.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.042929 
Standard error............: 0.07287 
Odds ratio (effect size)..: 0.958 
Lower 95% CI..............: 0.83 
Upper 95% CI..............: 1.105 
Z-value...................: -0.58912 
P-value...................: 0.5557806 
Hosmer and Lemeshow r^2...: 0.089912 
Cox and Snell r^2.........: 0.117078 
Nagelkerke's pseudo r^2...: 0.156177 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.025226 
Standard error............: 0.086273 
Odds ratio (effect size)..: 1.026 
Lower 95% CI..............: 0.866 
Upper 95% CI..............: 1.214 
Z-value...................: 0.292396 
P-value...................: 0.7699839 
Hosmer and Lemeshow r^2...: 0.0047 
Cox and Snell r^2.........: 0.004769 
Nagelkerke's pseudo r^2...: 0.007471 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.589849 
Standard error............: 0.091732 
Odds ratio (effect size)..: 0.554 
Lower 95% CI..............: 0.463 
Upper 95% CI..............: 0.664 
Z-value...................: -6.430145 
P-value...................: 1.274824e-10 
Hosmer and Lemeshow r^2...: 0.089749 
Cox and Snell r^2.........: 0.096839 
Nagelkerke's pseudo r^2...: 0.142717 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: IPH 
Effect size...............: -0.142143 
Standard error............: 0.073895 
Odds ratio (effect size)..: 0.867 
Lower 95% CI..............: 0.751 
Upper 95% CI..............: 1.003 
Z-value...................: -1.923563 
P-value...................: 0.0544094 
Hosmer and Lemeshow r^2...: 0.035592 
Cox and Snell r^2.........: 0.046671 
Nagelkerke's pseudo r^2...: 0.063163 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.197789 
Standard error............: 0.073026 
Odds ratio (effect size)..: 0.821 
Lower 95% CI..............: 0.711 
Upper 95% CI..............: 0.947 
Z-value...................: -2.708476 
P-value...................: 0.006759303 
Hosmer and Lemeshow r^2...: 0.029149 
Cox and Snell r^2.........: 0.039199 
Nagelkerke's pseudo r^2...: 0.05252 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.144548 
Standard error............: 0.074321 
Odds ratio (effect size)..: 1.156 
Lower 95% CI..............: 0.999 
Upper 95% CI..............: 1.337 
Z-value...................: 1.944908 
P-value...................: 0.051786 
Hosmer and Lemeshow r^2...: 0.022209 
Cox and Snell r^2.........: 0.027855 
Nagelkerke's pseudo r^2...: 0.038702 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of TMEM212.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.08514 
Standard error............: 0.075212 
Odds ratio (effect size)..: 0.918 
Lower 95% CI..............: 0.793 
Upper 95% CI..............: 1.064 
Z-value...................: -1.132003 
P-value...................: 0.2576331 
Hosmer and Lemeshow r^2...: 0.090622 
Cox and Snell r^2.........: 0.117945 
Nagelkerke's pseudo r^2...: 0.157334 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.050226 
Standard error............: 0.087653 
Odds ratio (effect size)..: 1.052 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.249 
Z-value...................: 0.573008 
P-value...................: 0.5666394 
Hosmer and Lemeshow r^2...: 0.004955 
Cox and Snell r^2.........: 0.005027 
Nagelkerke's pseudo r^2...: 0.007875 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.623947 
Standard error............: 0.09676 
Odds ratio (effect size)..: 0.536 
Lower 95% CI..............: 0.443 
Upper 95% CI..............: 0.648 
Z-value...................: -6.448384 
P-value...................: 1.130492e-10 
Hosmer and Lemeshow r^2...: 0.091093 
Cox and Snell r^2.........: 0.098215 
Nagelkerke's pseudo r^2...: 0.144745 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: IPH 
Effect size...............: -0.204489 
Standard error............: 0.076531 
Odds ratio (effect size)..: 0.815 
Lower 95% CI..............: 0.702 
Upper 95% CI..............: 0.947 
Z-value...................: -2.671991 
P-value...................: 0.007540258 
Hosmer and Lemeshow r^2...: 0.038436 
Cox and Snell r^2.........: 0.050304 
Nagelkerke's pseudo r^2...: 0.068081 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.203828 
Standard error............: 0.074514 
Odds ratio (effect size)..: 0.816 
Lower 95% CI..............: 0.705 
Upper 95% CI..............: 0.944 
Z-value...................: -2.735445 
P-value...................: 0.006229597 
Hosmer and Lemeshow r^2...: 0.029281 
Cox and Snell r^2.........: 0.039373 
Nagelkerke's pseudo r^2...: 0.052753 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.163038 
Standard error............: 0.075351 
Odds ratio (effect size)..: 1.177 
Lower 95% CI..............: 1.015 
Upper 95% CI..............: 1.364 
Z-value...................: 2.163716 
P-value...................: 0.03048614 
Hosmer and Lemeshow r^2...: 0.022964 
Cox and Snell r^2.........: 0.028788 
Nagelkerke's pseudo r^2...: 0.039998 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of PTPRD.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.076859 
Standard error............: 0.125987 
Odds ratio (effect size)..: 1.08 
Lower 95% CI..............: 0.844 
Upper 95% CI..............: 1.382 
Z-value...................: 0.610054 
P-value...................: 0.541826 
Hosmer and Lemeshow r^2...: 0.089931 
Cox and Snell r^2.........: 0.1171 
Nagelkerke's pseudo r^2...: 0.156208 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.035413 
Standard error............: 0.147738 
Odds ratio (effect size)..: 0.965 
Lower 95% CI..............: 0.723 
Upper 95% CI..............: 1.289 
Z-value...................: -0.239703 
P-value...................: 0.8105609 
Hosmer and Lemeshow r^2...: 0.004671 
Cox and Snell r^2.........: 0.00474 
Nagelkerke's pseudo r^2...: 0.007424 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.183767 
Standard error............: 0.143457 
Odds ratio (effect size)..: 0.832 
Lower 95% CI..............: 0.628 
Upper 95% CI..............: 1.102 
Z-value...................: -1.280987 
P-value...................: 0.2001983 
Hosmer and Lemeshow r^2...: 0.049859 
Cox and Snell r^2.........: 0.055013 
Nagelkerke's pseudo r^2...: 0.081075 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: IPH 
Effect size...............: -0.121151 
Standard error............: 0.125265 
Odds ratio (effect size)..: 0.886 
Lower 95% CI..............: 0.693 
Upper 95% CI..............: 1.132 
Z-value...................: -0.967158 
P-value...................: 0.3334651 
Hosmer and Lemeshow r^2...: 0.033358 
Cox and Snell r^2.........: 0.043806 
Nagelkerke's pseudo r^2...: 0.059286 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.156984 
Standard error............: 0.123005 
Odds ratio (effect size)..: 0.855 
Lower 95% CI..............: 0.672 
Upper 95% CI..............: 1.088 
Z-value...................: -1.276241 
P-value...................: 0.2018703 
Hosmer and Lemeshow r^2...: 0.024553 
Cox and Snell r^2.........: 0.033122 
Nagelkerke's pseudo r^2...: 0.044378 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.130025 
Standard error............: 0.130156 
Odds ratio (effect size)..: 0.878 
Lower 95% CI..............: 0.68 
Upper 95% CI..............: 1.133 
Z-value...................: -0.998997 
P-value...................: 0.3177962 
Hosmer and Lemeshow r^2...: 0.019849 
Cox and Snell r^2.........: 0.024932 
Nagelkerke's pseudo r^2...: 0.034641 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of CYP46A1.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.074981 
Standard error............: 0.072479 
Odds ratio (effect size)..: 0.928 
Lower 95% CI..............: 0.805 
Upper 95% CI..............: 1.069 
Z-value...................: -1.034524 
P-value...................: 0.3008911 
Hosmer and Lemeshow r^2...: 0.090463 
Cox and Snell r^2.........: 0.11775 
Nagelkerke's pseudo r^2...: 0.157074 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.011841 
Standard error............: 0.085286 
Odds ratio (effect size)..: 1.012 
Lower 95% CI..............: 0.856 
Upper 95% CI..............: 1.196 
Z-value...................: 0.138843 
P-value...................: 0.8895738 
Hosmer and Lemeshow r^2...: 0.00463 
Cox and Snell r^2.........: 0.004699 
Nagelkerke's pseudo r^2...: 0.00736 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.56117 
Standard error............: 0.096002 
Odds ratio (effect size)..: 0.571 
Lower 95% CI..............: 0.473 
Upper 95% CI..............: 0.689 
Z-value...................: -5.845389 
P-value...................: 5.053848e-09 
Hosmer and Lemeshow r^2...: 0.083893 
Cox and Snell r^2.........: 0.090816 
Nagelkerke's pseudo r^2...: 0.133841 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: IPH 
Effect size...............: -0.170953 
Standard error............: 0.074335 
Odds ratio (effect size)..: 0.843 
Lower 95% CI..............: 0.729 
Upper 95% CI..............: 0.975 
Z-value...................: -2.299768 
P-value...................: 0.02146137 
Hosmer and Lemeshow r^2...: 0.036932 
Cox and Snell r^2.........: 0.048384 
Nagelkerke's pseudo r^2...: 0.065482 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.207413 
Standard error............: 0.072776 
Odds ratio (effect size)..: 0.813 
Lower 95% CI..............: 0.705 
Upper 95% CI..............: 0.937 
Z-value...................: -2.850007 
P-value...................: 0.004371833 
Hosmer and Lemeshow r^2...: 0.029845 
Cox and Snell r^2.........: 0.040117 
Nagelkerke's pseudo r^2...: 0.05375 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.111652 
Standard error............: 0.072658 
Odds ratio (effect size)..: 1.118 
Lower 95% CI..............: 0.97 
Upper 95% CI..............: 1.289 
Z-value...................: 1.536681 
P-value...................: 0.1243715 
Hosmer and Lemeshow r^2...: 0.020986 
Cox and Snell r^2.........: 0.026342 
Nagelkerke's pseudo r^2...: 0.0366 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of OR2T33.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.056061 
Standard error............: 0.070342 
Odds ratio (effect size)..: 0.945 
Lower 95% CI..............: 0.824 
Upper 95% CI..............: 1.085 
Z-value...................: -0.796983 
P-value...................: 0.4254608 
Hosmer and Lemeshow r^2...: 0.090131 
Cox and Snell r^2.........: 0.117345 
Nagelkerke's pseudo r^2...: 0.156534 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.023531 
Standard error............: 0.082571 
Odds ratio (effect size)..: 1.024 
Lower 95% CI..............: 0.871 
Upper 95% CI..............: 1.204 
Z-value...................: 0.284975 
P-value...................: 0.775663 
Hosmer and Lemeshow r^2...: 0.004695 
Cox and Snell r^2.........: 0.004765 
Nagelkerke's pseudo r^2...: 0.007463 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.570899 
Standard error............: 0.091063 
Odds ratio (effect size)..: 0.565 
Lower 95% CI..............: 0.473 
Upper 95% CI..............: 0.675 
Z-value...................: -6.269249 
P-value...................: 3.62793e-10 
Hosmer and Lemeshow r^2...: 0.088667 
Cox and Snell r^2.........: 0.095729 
Nagelkerke's pseudo r^2...: 0.141081 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: IPH 
Effect size...............: -0.185993 
Standard error............: 0.071896 
Odds ratio (effect size)..: 0.83 
Lower 95% CI..............: 0.721 
Upper 95% CI..............: 0.956 
Z-value...................: -2.586981 
P-value...................: 0.009682103 
Hosmer and Lemeshow r^2...: 0.038073 
Cox and Snell r^2.........: 0.049841 
Nagelkerke's pseudo r^2...: 0.067453 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.17139 
Standard error............: 0.069782 
Odds ratio (effect size)..: 0.842 
Lower 95% CI..............: 0.735 
Upper 95% CI..............: 0.966 
Z-value...................: -2.456065 
P-value...................: 0.01404677 
Hosmer and Lemeshow r^2...: 0.028103 
Cox and Snell r^2.........: 0.037819 
Nagelkerke's pseudo r^2...: 0.050671 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.102619 
Standard error............: 0.070733 
Odds ratio (effect size)..: 1.108 
Lower 95% CI..............: 0.965 
Upper 95% CI..............: 1.273 
Z-value...................: 1.45079 
P-value...................: 0.1468382 
Hosmer and Lemeshow r^2...: 0.020775 
Cox and Snell r^2.........: 0.02608 
Nagelkerke's pseudo r^2...: 0.036236 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of SORBS2.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.18608 
Standard error............: 0.068317 
Odds ratio (effect size)..: 1.205 
Lower 95% CI..............: 1.054 
Upper 95% CI..............: 1.377 
Z-value...................: 2.723764 
P-value...................: 0.006454267 
Hosmer and Lemeshow r^2...: 0.095321 
Cox and Snell r^2.........: 0.123667 
Nagelkerke's pseudo r^2...: 0.164966 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.047212 
Standard error............: 0.080536 
Odds ratio (effect size)..: 1.048 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.228 
Z-value...................: 0.586227 
P-value...................: 0.5577228 
Hosmer and Lemeshow r^2...: 0.004976 
Cox and Snell r^2.........: 0.005049 
Nagelkerke's pseudo r^2...: 0.007908 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.187482 
Standard error............: 0.072791 
Odds ratio (effect size)..: 0.829 
Lower 95% CI..............: 0.719 
Upper 95% CI..............: 0.956 
Z-value...................: -2.575625 
P-value...................: 0.0100059 
Hosmer and Lemeshow r^2...: 0.054339 
Cox and Snell r^2.........: 0.059804 
Nagelkerke's pseudo r^2...: 0.088137 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: IPH 
Effect size...............: -0.091194 
Standard error............: 0.06645 
Odds ratio (effect size)..: 0.913 
Lower 95% CI..............: 0.801 
Upper 95% CI..............: 1.04 
Z-value...................: -1.372359 
P-value...................: 0.1699518 
Hosmer and Lemeshow r^2...: 0.034104 
Cox and Snell r^2.........: 0.044763 
Nagelkerke's pseudo r^2...: 0.060581 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.278333 
Standard error............: 0.066413 
Odds ratio (effect size)..: 0.757 
Lower 95% CI..............: 0.665 
Upper 95% CI..............: 0.862 
Z-value...................: -4.190908 
P-value...................: 2.778408e-05 
Hosmer and Lemeshow r^2...: 0.037348 
Cox and Snell r^2.........: 0.049945 
Nagelkerke's pseudo r^2...: 0.066918 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.076011 
Standard error............: 0.070197 
Odds ratio (effect size)..: 1.079 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 1.238 
Z-value...................: 1.082829 
P-value...................: 0.2788845 
Hosmer and Lemeshow r^2...: 0.02 
Cox and Snell r^2.........: 0.025119 
Nagelkerke's pseudo r^2...: 0.0349 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of ITGA7.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.077291 
Standard error............: 0.081752 
Odds ratio (effect size)..: 0.926 
Lower 95% CI..............: 0.789 
Upper 95% CI..............: 1.086 
Z-value...................: -0.945437 
P-value...................: 0.3444359 
Hosmer and Lemeshow r^2...: 0.090326 
Cox and Snell r^2.........: 0.117583 
Nagelkerke's pseudo r^2...: 0.156851 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.063294 
Standard error............: 0.096241 
Odds ratio (effect size)..: 1.065 
Lower 95% CI..............: 0.882 
Upper 95% CI..............: 1.287 
Z-value...................: 0.657665 
P-value...................: 0.5107535 
Hosmer and Lemeshow r^2...: 0.005065 
Cox and Snell r^2.........: 0.005139 
Nagelkerke's pseudo r^2...: 0.00805 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.488635 
Standard error............: 0.09836 
Odds ratio (effect size)..: 0.613 
Lower 95% CI..............: 0.506 
Upper 95% CI..............: 0.744 
Z-value...................: -4.967812 
P-value...................: 6.771242e-07 
Hosmer and Lemeshow r^2...: 0.072294 
Cox and Snell r^2.........: 0.078769 
Nagelkerke's pseudo r^2...: 0.116087 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: IPH 
Effect size...............: -0.138097 
Standard error............: 0.08193 
Odds ratio (effect size)..: 0.871 
Lower 95% CI..............: 0.742 
Upper 95% CI..............: 1.023 
Z-value...................: -1.685548 
P-value...................: 0.09188293 
Hosmer and Lemeshow r^2...: 0.034889 
Cox and Snell r^2.........: 0.04577 
Nagelkerke's pseudo r^2...: 0.061944 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.191919 
Standard error............: 0.080841 
Odds ratio (effect size)..: 0.825 
Lower 95% CI..............: 0.704 
Upper 95% CI..............: 0.967 
Z-value...................: -2.374019 
P-value...................: 0.01759564 
Hosmer and Lemeshow r^2...: 0.027754 
Cox and Snell r^2.........: 0.037359 
Nagelkerke's pseudo r^2...: 0.050055 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.119674 
Standard error............: 0.083101 
Odds ratio (effect size)..: 1.127 
Lower 95% CI..............: 0.958 
Upper 95% CI..............: 1.327 
Z-value...................: 1.440113 
P-value...................: 0.1498354 
Hosmer and Lemeshow r^2...: 0.020757 
Cox and Snell r^2.........: 0.026058 
Nagelkerke's pseudo r^2...: 0.036205 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of FOS.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.01943 
Standard error............: 0.040563 
Odds ratio (effect size)..: 0.981 
Lower 95% CI..............: 0.906 
Upper 95% CI..............: 1.062 
Z-value...................: -0.479008 
P-value...................: 0.6319326 
Hosmer and Lemeshow r^2...: 0.089823 
Cox and Snell r^2.........: 0.116969 
Nagelkerke's pseudo r^2...: 0.156032 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.001773 
Standard error............: 0.047527 
Odds ratio (effect size)..: 0.998 
Lower 95% CI..............: 0.909 
Upper 95% CI..............: 1.096 
Z-value...................: -0.037312 
P-value...................: 0.9702359 
Hosmer and Lemeshow r^2...: 0.004611 
Cox and Snell r^2.........: 0.00468 
Nagelkerke's pseudo r^2...: 0.00733 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.187976 
Standard error............: 0.047031 
Odds ratio (effect size)..: 1.207 
Lower 95% CI..............: 1.101 
Upper 95% CI..............: 1.323 
Z-value...................: 3.996897 
P-value...................: 6.417809e-05 
Hosmer and Lemeshow r^2...: 0.063663 
Cox and Snell r^2.........: 0.069701 
Nagelkerke's pseudo r^2...: 0.102723 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: IPH 
Effect size...............: 0.087464 
Standard error............: 0.040468 
Odds ratio (effect size)..: 1.091 
Lower 95% CI..............: 1.008 
Upper 95% CI..............: 1.181 
Z-value...................: 2.161335 
P-value...................: 0.03066946 
Hosmer and Lemeshow r^2...: 0.036368 
Cox and Snell r^2.........: 0.047663 
Nagelkerke's pseudo r^2...: 0.064505 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.093959 
Standard error............: 0.039993 
Odds ratio (effect size)..: 1.099 
Lower 95% CI..............: 1.016 
Upper 95% CI..............: 1.188 
Z-value...................: 2.349377 
P-value...................: 0.01880486 
Hosmer and Lemeshow r^2...: 0.027649 
Cox and Snell r^2.........: 0.03722 
Nagelkerke's pseudo r^2...: 0.049869 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.096808 
Standard error............: 0.041449 
Odds ratio (effect size)..: 0.908 
Lower 95% CI..............: 0.837 
Upper 95% CI..............: 0.985 
Z-value...................: -2.335609 
P-value...................: 0.01951165 
Hosmer and Lemeshow r^2...: 0.023631 
Cox and Snell r^2.........: 0.029612 
Nagelkerke's pseudo r^2...: 0.041143 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of HMOX1.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.130552 
Standard error............: 0.063643 
Odds ratio (effect size)..: 0.878 
Lower 95% CI..............: 0.775 
Upper 95% CI..............: 0.994 
Z-value...................: -2.051314 
P-value...................: 0.04023639 
Hosmer and Lemeshow r^2...: 0.092855 
Cox and Snell r^2.........: 0.120668 
Nagelkerke's pseudo r^2...: 0.160967 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.041202 
Standard error............: 0.07341 
Odds ratio (effect size)..: 0.96 
Lower 95% CI..............: 0.831 
Upper 95% CI..............: 1.108 
Z-value...................: -0.561263 
P-value...................: 0.5746184 
Hosmer and Lemeshow r^2...: 0.004939 
Cox and Snell r^2.........: 0.005012 
Nagelkerke's pseudo r^2...: 0.00785 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.450197 
Standard error............: 0.084334 
Odds ratio (effect size)..: 1.569 
Lower 95% CI..............: 1.33 
Upper 95% CI..............: 1.851 
Z-value...................: 5.338291 
P-value...................: 9.382681e-08 
Hosmer and Lemeshow r^2...: 0.078738 
Cox and Snell r^2.........: 0.085481 
Nagelkerke's pseudo r^2...: 0.125979 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: IPH 
Effect size...............: 0.224506 
Standard error............: 0.065925 
Odds ratio (effect size)..: 1.252 
Lower 95% CI..............: 1.1 
Upper 95% CI..............: 1.424 
Z-value...................: 3.405467 
P-value...................: 0.0006605107 
Hosmer and Lemeshow r^2...: 0.04227 
Cox and Snell r^2.........: 0.05518 
Nagelkerke's pseudo r^2...: 0.074679 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.266618 
Standard error............: 0.06542 
Odds ratio (effect size)..: 1.306 
Lower 95% CI..............: 1.148 
Upper 95% CI..............: 1.484 
Z-value...................: 4.075488 
P-value...................: 4.591796e-05 
Hosmer and Lemeshow r^2...: 0.037037 
Cox and Snell r^2.........: 0.049541 
Nagelkerke's pseudo r^2...: 0.066376 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.161631 
Standard error............: 0.063211 
Odds ratio (effect size)..: 0.851 
Lower 95% CI..............: 0.752 
Upper 95% CI..............: 0.963 
Z-value...................: -2.557024 
P-value...................: 0.01055718 
Hosmer and Lemeshow r^2...: 0.024515 
Cox and Snell r^2.........: 0.030702 
Nagelkerke's pseudo r^2...: 0.042657 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of LAPTM5.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.06423 
Standard error............: 0.032114 
Odds ratio (effect size)..: 0.938 
Lower 95% CI..............: 0.881 
Upper 95% CI..............: 0.999 
Z-value...................: -2.000054 
P-value...................: 0.04549438 
Hosmer and Lemeshow r^2...: 0.092676 
Cox and Snell r^2.........: 0.12045 
Nagelkerke's pseudo r^2...: 0.160676 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.00078 
Standard error............: 0.037876 
Odds ratio (effect size)..: 1.001 
Lower 95% CI..............: 0.929 
Upper 95% CI..............: 1.078 
Z-value...................: 0.020602 
P-value...................: 0.983563 
Hosmer and Lemeshow r^2...: 0.00461 
Cox and Snell r^2.........: 0.004679 
Nagelkerke's pseudo r^2...: 0.007329 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.211779 
Standard error............: 0.037885 
Odds ratio (effect size)..: 1.236 
Lower 95% CI..............: 1.147 
Upper 95% CI..............: 1.331 
Z-value...................: 5.590039 
P-value...................: 2.270182e-08 
Hosmer and Lemeshow r^2...: 0.078722 
Cox and Snell r^2.........: 0.085465 
Nagelkerke's pseudo r^2...: 0.125955 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: IPH 
Effect size...............: 0.01723 
Standard error............: 0.031805 
Odds ratio (effect size)..: 1.017 
Lower 95% CI..............: 0.956 
Upper 95% CI..............: 1.083 
Z-value...................: 0.541755 
P-value...................: 0.5879876 
Hosmer and Lemeshow r^2...: 0.032842 
Cox and Snell r^2.........: 0.043143 
Nagelkerke's pseudo r^2...: 0.058388 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.110583 
Standard error............: 0.031751 
Odds ratio (effect size)..: 1.117 
Lower 95% CI..............: 1.05 
Upper 95% CI..............: 1.189 
Z-value...................: 3.4828 
P-value...................: 0.0004961987 
Hosmer and Lemeshow r^2...: 0.032958 
Cox and Snell r^2.........: 0.044207 
Nagelkerke's pseudo r^2...: 0.05923 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.053214 
Standard error............: 0.032846 
Odds ratio (effect size)..: 0.948 
Lower 95% CI..............: 0.889 
Upper 95% CI..............: 1.011 
Z-value...................: -1.620118 
P-value...................: 0.105207 
Hosmer and Lemeshow r^2...: 0.021227 
Cox and Snell r^2.........: 0.02664 
Nagelkerke's pseudo r^2...: 0.037014 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of MMP9.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.074887 
Standard error............: 0.034174 
Odds ratio (effect size)..: 0.928 
Lower 95% CI..............: 0.868 
Upper 95% CI..............: 0.992 
Z-value...................: -2.191376 
P-value...................: 0.02842458 
Hosmer and Lemeshow r^2...: 0.093287 
Cox and Snell r^2.........: 0.121194 
Nagelkerke's pseudo r^2...: 0.161668 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.016965 
Standard error............: 0.039876 
Odds ratio (effect size)..: 0.983 
Lower 95% CI..............: 0.909 
Upper 95% CI..............: 1.063 
Z-value...................: -0.425455 
P-value...................: 0.6705048 
Hosmer and Lemeshow r^2...: 0.0048 
Cox and Snell r^2.........: 0.004871 
Nagelkerke's pseudo r^2...: 0.00763 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.244548 
Standard error............: 0.042114 
Odds ratio (effect size)..: 1.277 
Lower 95% CI..............: 1.176 
Upper 95% CI..............: 1.387 
Z-value...................: 5.806753 
P-value...................: 6.369591e-09 
Hosmer and Lemeshow r^2...: 0.082447 
Cox and Snell r^2.........: 0.089323 
Nagelkerke's pseudo r^2...: 0.131641 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: IPH 
Effect size...............: 0.010868 
Standard error............: 0.033661 
Odds ratio (effect size)..: 1.011 
Lower 95% CI..............: 0.946 
Upper 95% CI..............: 1.08 
Z-value...................: 0.322858 
P-value...................: 0.7468028 
Hosmer and Lemeshow r^2...: 0.032691 
Cox and Snell r^2.........: 0.042949 
Nagelkerke's pseudo r^2...: 0.058126 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.134466 
Standard error............: 0.034053 
Odds ratio (effect size)..: 1.144 
Lower 95% CI..............: 1.07 
Upper 95% CI..............: 1.223 
Z-value...................: 3.948783 
P-value...................: 7.85496e-05 
Hosmer and Lemeshow r^2...: 0.035854 
Cox and Snell r^2.........: 0.047997 
Nagelkerke's pseudo r^2...: 0.064308 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.089603 
Standard error............: 0.034624 
Odds ratio (effect size)..: 0.914 
Lower 95% CI..............: 0.854 
Upper 95% CI..............: 0.978 
Z-value...................: -2.587914 
P-value...................: 0.009655895 
Hosmer and Lemeshow r^2...: 0.024674 
Cox and Snell r^2.........: 0.030899 
Nagelkerke's pseudo r^2...: 0.042931 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of SIGLEC1.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.050993 
Standard error............: 0.037868 
Odds ratio (effect size)..: 0.95 
Lower 95% CI..............: 0.882 
Upper 95% CI..............: 1.023 
Z-value...................: -1.3466 
P-value...................: 0.1781089 
Hosmer and Lemeshow r^2...: 0.091019 
Cox and Snell r^2.........: 0.118429 
Nagelkerke's pseudo r^2...: 0.15798 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.013743 
Standard error............: 0.044572 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.904 
Upper 95% CI..............: 1.076 
Z-value...................: -0.308335 
P-value...................: 0.7578273 
Hosmer and Lemeshow r^2...: 0.00471 
Cox and Snell r^2.........: 0.00478 
Nagelkerke's pseudo r^2...: 0.007487 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.23782 
Standard error............: 0.045461 
Odds ratio (effect size)..: 1.268 
Lower 95% CI..............: 1.16 
Upper 95% CI..............: 1.387 
Z-value...................: 5.231354 
P-value...................: 1.682727e-07 
Hosmer and Lemeshow r^2...: 0.075136 
Cox and Snell r^2.........: 0.081736 
Nagelkerke's pseudo r^2...: 0.120459 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: IPH 
Effect size...............: 0.034127 
Standard error............: 0.037684 
Odds ratio (effect size)..: 1.035 
Lower 95% CI..............: 0.961 
Upper 95% CI..............: 1.114 
Z-value...................: 0.905632 
P-value...................: 0.3651307 
Hosmer and Lemeshow r^2...: 0.033263 
Cox and Snell r^2.........: 0.043684 
Nagelkerke's pseudo r^2...: 0.059121 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.115673 
Standard error............: 0.037479 
Odds ratio (effect size)..: 1.123 
Lower 95% CI..............: 1.043 
Upper 95% CI..............: 1.208 
Z-value...................: 3.08637 
P-value...................: 0.002026167 
Hosmer and Lemeshow r^2...: 0.030873 
Cox and Snell r^2.........: 0.041469 
Nagelkerke's pseudo r^2...: 0.055561 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.083676 
Standard error............: 0.038642 
Odds ratio (effect size)..: 0.92 
Lower 95% CI..............: 0.853 
Upper 95% CI..............: 0.992 
Z-value...................: -2.165397 
P-value...................: 0.03035727 
Hosmer and Lemeshow r^2...: 0.02298 
Cox and Snell r^2.........: 0.028808 
Nagelkerke's pseudo r^2...: 0.040026 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of FTL.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.059386 
Standard error............: 0.03214 
Odds ratio (effect size)..: 0.942 
Lower 95% CI..............: 0.885 
Upper 95% CI..............: 1.004 
Z-value...................: -1.847736 
P-value...................: 0.0646406 
Hosmer and Lemeshow r^2...: 0.092234 
Cox and Snell r^2.........: 0.119912 
Nagelkerke's pseudo r^2...: 0.159958 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.015922 
Standard error............: 0.037982 
Odds ratio (effect size)..: 1.016 
Lower 95% CI..............: 0.943 
Upper 95% CI..............: 1.095 
Z-value...................: 0.419201 
P-value...................: 0.6750691 
Hosmer and Lemeshow r^2...: 0.004795 
Cox and Snell r^2.........: 0.004865 
Nagelkerke's pseudo r^2...: 0.007621 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.164925 
Standard error............: 0.035998 
Odds ratio (effect size)..: 1.179 
Lower 95% CI..............: 1.099 
Upper 95% CI..............: 1.266 
Z-value...................: 4.581471 
P-value...................: 4.617172e-06 
Hosmer and Lemeshow r^2...: 0.067938 
Cox and Snell r^2.........: 0.074204 
Nagelkerke's pseudo r^2...: 0.109358 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: IPH 
Effect size...............: 0.011028 
Standard error............: 0.031862 
Odds ratio (effect size)..: 1.011 
Lower 95% CI..............: 0.95 
Upper 95% CI..............: 1.076 
Z-value...................: 0.346117 
P-value...................: 0.7292548 
Hosmer and Lemeshow r^2...: 0.032703 
Cox and Snell r^2.........: 0.042965 
Nagelkerke's pseudo r^2...: 0.058147 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.095075 
Standard error............: 0.031584 
Odds ratio (effect size)..: 1.1 
Lower 95% CI..............: 1.034 
Upper 95% CI..............: 1.17 
Z-value...................: 3.0102 
P-value...................: 0.002610757 
Hosmer and Lemeshow r^2...: 0.030474 
Cox and Snell r^2.........: 0.040944 
Nagelkerke's pseudo r^2...: 0.054858 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.054511 
Standard error............: 0.033319 
Odds ratio (effect size)..: 0.947 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.011 
Z-value...................: -1.636036 
P-value...................: 0.101832 
Hosmer and Lemeshow r^2...: 0.021287 
Cox and Snell r^2.........: 0.026715 
Nagelkerke's pseudo r^2...: 0.037117 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of CD14.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.057083 
Standard error............: 0.032607 
Odds ratio (effect size)..: 0.945 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.007 
Z-value...................: -1.750626 
P-value...................: 0.08001042 
Hosmer and Lemeshow r^2...: 0.091967 
Cox and Snell r^2.........: 0.119586 
Nagelkerke's pseudo r^2...: 0.159523 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.002244 
Standard error............: 0.038493 
Odds ratio (effect size)..: 1.002 
Lower 95% CI..............: 0.929 
Upper 95% CI..............: 1.081 
Z-value...................: 0.058307 
P-value...................: 0.9535039 
Hosmer and Lemeshow r^2...: 0.004613 
Cox and Snell r^2.........: 0.004682 
Nagelkerke's pseudo r^2...: 0.007334 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.207223 
Standard error............: 0.038164 
Odds ratio (effect size)..: 1.23 
Lower 95% CI..............: 1.142 
Upper 95% CI..............: 1.326 
Z-value...................: 5.429805 
P-value...................: 5.64158e-08 
Hosmer and Lemeshow r^2...: 0.076876 
Cox and Snell r^2.........: 0.083548 
Nagelkerke's pseudo r^2...: 0.123129 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: IPH 
Effect size...............: 0.022195 
Standard error............: 0.032308 
Odds ratio (effect size)..: 1.022 
Lower 95% CI..............: 0.96 
Upper 95% CI..............: 1.089 
Z-value...................: 0.686966 
P-value...................: 0.4921043 
Hosmer and Lemeshow r^2...: 0.032984 
Cox and Snell r^2.........: 0.043326 
Nagelkerke's pseudo r^2...: 0.058636 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.105765 
Standard error............: 0.032199 
Odds ratio (effect size)..: 1.112 
Lower 95% CI..............: 1.044 
Upper 95% CI..............: 1.184 
Z-value...................: 3.28468 
P-value...................: 0.001020981 
Hosmer and Lemeshow r^2...: 0.031879 
Cox and Snell r^2.........: 0.042791 
Nagelkerke's pseudo r^2...: 0.057332 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.053002 
Standard error............: 0.033335 
Odds ratio (effect size)..: 0.948 
Lower 95% CI..............: 0.888 
Upper 95% CI..............: 1.012 
Z-value...................: -1.589957 
P-value...................: 0.1118446 
Hosmer and Lemeshow r^2...: 0.021145 
Cox and Snell r^2.........: 0.026538 
Nagelkerke's pseudo r^2...: 0.036872 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of HCST.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.066796 
Standard error............: 0.033195 
Odds ratio (effect size)..: 0.935 
Lower 95% CI..............: 0.876 
Upper 95% CI..............: 0.998 
Z-value...................: -2.012232 
P-value...................: 0.04419547 
Hosmer and Lemeshow r^2...: 0.092713 
Cox and Snell r^2.........: 0.120495 
Nagelkerke's pseudo r^2...: 0.160736 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.010488 
Standard error............: 0.039191 
Odds ratio (effect size)..: 1.011 
Lower 95% CI..............: 0.936 
Upper 95% CI..............: 1.091 
Z-value...................: 0.267599 
P-value...................: 0.7890083 
Hosmer and Lemeshow r^2...: 0.004685 
Cox and Snell r^2.........: 0.004755 
Nagelkerke's pseudo r^2...: 0.007448 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.178208 
Standard error............: 0.038182 
Odds ratio (effect size)..: 1.195 
Lower 95% CI..............: 1.109 
Upper 95% CI..............: 1.288 
Z-value...................: 4.667274 
P-value...................: 3.052224e-06 
Hosmer and Lemeshow r^2...: 0.069083 
Cox and Snell r^2.........: 0.075406 
Nagelkerke's pseudo r^2...: 0.11113 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: IPH 
Effect size...............: 0.009391 
Standard error............: 0.032858 
Odds ratio (effect size)..: 1.009 
Lower 95% CI..............: 0.946 
Upper 95% CI..............: 1.077 
Z-value...................: 0.285801 
P-value...................: 0.7750307 
Hosmer and Lemeshow r^2...: 0.032673 
Cox and Snell r^2.........: 0.042926 
Nagelkerke's pseudo r^2...: 0.058094 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.112807 
Standard error............: 0.032784 
Odds ratio (effect size)..: 1.119 
Lower 95% CI..............: 1.05 
Upper 95% CI..............: 1.194 
Z-value...................: 3.440886 
P-value...................: 0.0005798135 
Hosmer and Lemeshow r^2...: 0.032727 
Cox and Snell r^2.........: 0.043904 
Nagelkerke's pseudo r^2...: 0.058823 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.042191 
Standard error............: 0.033948 
Odds ratio (effect size)..: 0.959 
Lower 95% CI..............: 0.897 
Upper 95% CI..............: 1.025 
Z-value...................: -1.242818 
P-value...................: 0.2139351 
Hosmer and Lemeshow r^2...: 0.020308 
Cox and Snell r^2.........: 0.025501 
Nagelkerke's pseudo r^2...: 0.035431 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of TIMP3.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.006759 
Standard error............: 0.037133 
Odds ratio (effect size)..: 0.993 
Lower 95% CI..............: 0.924 
Upper 95% CI..............: 1.068 
Z-value...................: -0.182011 
P-value...................: 0.8555738 
Hosmer and Lemeshow r^2...: 0.089675 
Cox and Snell r^2.........: 0.116788 
Nagelkerke's pseudo r^2...: 0.15579 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.014044 
Standard error............: 0.043757 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.905 
Upper 95% CI..............: 1.074 
Z-value...................: -0.320957 
P-value...................: 0.7482432 
Hosmer and Lemeshow r^2...: 0.004718 
Cox and Snell r^2.........: 0.004788 
Nagelkerke's pseudo r^2...: 0.0075 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.188531 
Standard error............: 0.043786 
Odds ratio (effect size)..: 1.207 
Lower 95% CI..............: 1.108 
Upper 95% CI..............: 1.316 
Z-value...................: 4.305789 
P-value...................: 1.663918e-05 
Hosmer and Lemeshow r^2...: 0.066255 
Cox and Snell r^2.........: 0.072433 
Nagelkerke's pseudo r^2...: 0.106749 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: IPH 
Effect size...............: 0.054645 
Standard error............: 0.037179 
Odds ratio (effect size)..: 1.056 
Lower 95% CI..............: 0.982 
Upper 95% CI..............: 1.136 
Z-value...................: 1.46978 
P-value...................: 0.1416213 
Hosmer and Lemeshow r^2...: 0.034342 
Cox and Snell r^2.........: 0.045068 
Nagelkerke's pseudo r^2...: 0.060994 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.115963 
Standard error............: 0.037101 
Odds ratio (effect size)..: 1.123 
Lower 95% CI..............: 1.044 
Upper 95% CI..............: 1.208 
Z-value...................: 3.125601 
P-value...................: 0.001774423 
Hosmer and Lemeshow r^2...: 0.031103 
Cox and Snell r^2.........: 0.041771 
Nagelkerke's pseudo r^2...: 0.055966 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.091839 
Standard error............: 0.037844 
Odds ratio (effect size)..: 0.912 
Lower 95% CI..............: 0.847 
Upper 95% CI..............: 0.982 
Z-value...................: -2.426761 
P-value...................: 0.01523429 
Hosmer and Lemeshow r^2...: 0.023997 
Cox and Snell r^2.........: 0.030064 
Nagelkerke's pseudo r^2...: 0.041771 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of CCL2.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.048556 
Standard error............: 0.039603 
Odds ratio (effect size)..: 0.953 
Lower 95% CI..............: 0.881 
Upper 95% CI..............: 1.029 
Z-value...................: -1.226075 
P-value...................: 0.2201705 
Hosmer and Lemeshow r^2...: 0.090785 
Cox and Snell r^2.........: 0.118144 
Nagelkerke's pseudo r^2...: 0.1576 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.018857 
Standard error............: 0.046716 
Odds ratio (effect size)..: 0.981 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.075 
Z-value...................: -0.403662 
P-value...................: 0.6864612 
Hosmer and Lemeshow r^2...: 0.004781 
Cox and Snell r^2.........: 0.004852 
Nagelkerke's pseudo r^2...: 0.0076 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.223825 
Standard error............: 0.046291 
Odds ratio (effect size)..: 1.251 
Lower 95% CI..............: 1.142 
Upper 95% CI..............: 1.37 
Z-value...................: 4.83514 
P-value...................: 1.33052e-06 
Hosmer and Lemeshow r^2...: 0.070915 
Cox and Snell r^2.........: 0.077326 
Nagelkerke's pseudo r^2...: 0.11396 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: IPH 
Effect size...............: 0.043925 
Standard error............: 0.039304 
Odds ratio (effect size)..: 1.045 
Lower 95% CI..............: 0.967 
Upper 95% CI..............: 1.129 
Z-value...................: 1.117558 
P-value...................: 0.2637557 
Hosmer and Lemeshow r^2...: 0.033606 
Cox and Snell r^2.........: 0.044125 
Nagelkerke's pseudo r^2...: 0.059717 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.061223 
Standard error............: 0.038754 
Odds ratio (effect size)..: 1.063 
Lower 95% CI..............: 0.985 
Upper 95% CI..............: 1.147 
Z-value...................: 1.579804 
P-value...................: 0.1141517 
Hosmer and Lemeshow r^2...: 0.025234 
Cox and Snell r^2.........: 0.034025 
Nagelkerke's pseudo r^2...: 0.045588 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.073586 
Standard error............: 0.040586 
Odds ratio (effect size)..: 0.929 
Lower 95% CI..............: 0.858 
Upper 95% CI..............: 1.006 
Z-value...................: -1.813109 
P-value...................: 0.06981495 
Hosmer and Lemeshow r^2...: 0.021789 
Cox and Snell r^2.........: 0.027336 
Nagelkerke's pseudo r^2...: 0.037981 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of SAT1.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.053925 
Standard error............: 0.03366 
Odds ratio (effect size)..: 0.948 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.012 
Z-value...................: -1.602052 
P-value...................: 0.1091442 
Hosmer and Lemeshow r^2...: 0.091589 
Cox and Snell r^2.........: 0.119125 
Nagelkerke's pseudo r^2...: 0.158909 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.003459 
Standard error............: 0.039946 
Odds ratio (effect size)..: 1.003 
Lower 95% CI..............: 0.928 
Upper 95% CI..............: 1.085 
Z-value...................: 0.086592 
P-value...................: 0.930996 
Hosmer and Lemeshow r^2...: 0.004618 
Cox and Snell r^2.........: 0.004686 
Nagelkerke's pseudo r^2...: 0.007341 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.181957 
Standard error............: 0.039173 
Odds ratio (effect size)..: 1.2 
Lower 95% CI..............: 1.111 
Upper 95% CI..............: 1.295 
Z-value...................: 4.644933 
P-value...................: 3.401873e-06 
Hosmer and Lemeshow r^2...: 0.068922 
Cox and Snell r^2.........: 0.075237 
Nagelkerke's pseudo r^2...: 0.110882 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: IPH 
Effect size...............: 0.028265 
Standard error............: 0.033486 
Odds ratio (effect size)..: 1.029 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.098 
Z-value...................: 0.844072 
P-value...................: 0.3986291 
Hosmer and Lemeshow r^2...: 0.033176 
Cox and Snell r^2.........: 0.043573 
Nagelkerke's pseudo r^2...: 0.05897 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.098454 
Standard error............: 0.033227 
Odds ratio (effect size)..: 1.103 
Lower 95% CI..............: 1.034 
Upper 95% CI..............: 1.178 
Z-value...................: 2.963033 
P-value...................: 0.00304624 
Hosmer and Lemeshow r^2...: 0.030257 
Cox and Snell r^2.........: 0.040659 
Nagelkerke's pseudo r^2...: 0.054476 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.077845 
Standard error............: 0.034741 
Odds ratio (effect size)..: 0.925 
Lower 95% CI..............: 0.864 
Upper 95% CI..............: 0.99 
Z-value...................: -2.240755 
P-value...................: 0.02504193 
Hosmer and Lemeshow r^2...: 0.023282 
Cox and Snell r^2.........: 0.029182 
Nagelkerke's pseudo r^2...: 0.040545 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of CD163.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.070342 
Standard error............: 0.050214 
Odds ratio (effect size)..: 0.932 
Lower 95% CI..............: 0.845 
Upper 95% CI..............: 1.028 
Z-value...................: -1.400832 
P-value...................: 0.1612644 
Hosmer and Lemeshow r^2...: 0.091135 
Cox and Snell r^2.........: 0.118571 
Nagelkerke's pseudo r^2...: 0.158169 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.03519 
Standard error............: 0.058405 
Odds ratio (effect size)..: 0.965 
Lower 95% CI..............: 0.861 
Upper 95% CI..............: 1.083 
Z-value...................: -0.602517 
P-value...................: 0.5468298 
Hosmer and Lemeshow r^2...: 0.004991 
Cox and Snell r^2.........: 0.005064 
Nagelkerke's pseudo r^2...: 0.007933 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.351385 
Standard error............: 0.061804 
Odds ratio (effect size)..: 1.421 
Lower 95% CI..............: 1.259 
Upper 95% CI..............: 1.604 
Z-value...................: 5.685512 
P-value...................: 1.304212e-08 
Hosmer and Lemeshow r^2...: 0.081174 
Cox and Snell r^2.........: 0.088007 
Nagelkerke's pseudo r^2...: 0.1297 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: IPH 
Effect size...............: 0.077476 
Standard error............: 0.049826 
Odds ratio (effect size)..: 1.081 
Lower 95% CI..............: 0.98 
Upper 95% CI..............: 1.191 
Z-value...................: 1.554923 
P-value...................: 0.1199644 
Hosmer and Lemeshow r^2...: 0.03455 
Cox and Snell r^2.........: 0.045335 
Nagelkerke's pseudo r^2...: 0.061355 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.166326 
Standard error............: 0.04992 
Odds ratio (effect size)..: 1.181 
Lower 95% CI..............: 1.071 
Upper 95% CI..............: 1.302 
Z-value...................: 3.331875 
P-value...................: 0.0008626285 
Hosmer and Lemeshow r^2...: 0.0322 
Cox and Snell r^2.........: 0.043212 
Nagelkerke's pseudo r^2...: 0.057897 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.085158 
Standard error............: 0.050582 
Odds ratio (effect size)..: 0.918 
Lower 95% CI..............: 0.832 
Upper 95% CI..............: 1.014 
Z-value...................: -1.683577 
P-value...................: 0.09226347 
Hosmer and Lemeshow r^2...: 0.021396 
Cox and Snell r^2.........: 0.026849 
Nagelkerke's pseudo r^2...: 0.037303 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of PTGDS.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.001175 
Standard error............: 0.043781 
Odds ratio (effect size)..: 1.001 
Lower 95% CI..............: 0.919 
Upper 95% CI..............: 1.091 
Z-value...................: 0.026838 
P-value...................: 0.9785891 
Hosmer and Lemeshow r^2...: 0.089651 
Cox and Snell r^2.........: 0.116758 
Nagelkerke's pseudo r^2...: 0.155751 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.024449 
Standard error............: 0.052344 
Odds ratio (effect size)..: 1.025 
Lower 95% CI..............: 0.925 
Upper 95% CI..............: 1.135 
Z-value...................: 0.467088 
P-value...................: 0.640437 
Hosmer and Lemeshow r^2...: 0.004841 
Cox and Snell r^2.........: 0.004913 
Nagelkerke's pseudo r^2...: 0.007695 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.232826 
Standard error............: 0.054775 
Odds ratio (effect size)..: 1.262 
Lower 95% CI..............: 1.134 
Upper 95% CI..............: 1.405 
Z-value...................: 4.250624 
P-value...................: 2.131758e-05 
Hosmer and Lemeshow r^2...: 0.066343 
Cox and Snell r^2.........: 0.072526 
Nagelkerke's pseudo r^2...: 0.106886 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: IPH 
Effect size...............: 0.064694 
Standard error............: 0.044296 
Odds ratio (effect size)..: 1.067 
Lower 95% CI..............: 0.978 
Upper 95% CI..............: 1.164 
Z-value...................: 1.460484 
P-value...................: 0.1441572 
Hosmer and Lemeshow r^2...: 0.034327 
Cox and Snell r^2.........: 0.045049 
Nagelkerke's pseudo r^2...: 0.060968 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.155392 
Standard error............: 0.044489 
Odds ratio (effect size)..: 1.168 
Lower 95% CI..............: 1.071 
Upper 95% CI..............: 1.275 
Z-value...................: 3.492857 
P-value...................: 0.0004778821 
Hosmer and Lemeshow r^2...: 0.033196 
Cox and Snell r^2.........: 0.044519 
Nagelkerke's pseudo r^2...: 0.059647 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.071357 
Standard error............: 0.044228 
Odds ratio (effect size)..: 0.931 
Lower 95% CI..............: 0.854 
Upper 95% CI..............: 1.015 
Z-value...................: -1.613379 
P-value...................: 0.1066623 
Hosmer and Lemeshow r^2...: 0.021192 
Cox and Snell r^2.........: 0.026597 
Nagelkerke's pseudo r^2...: 0.036954 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of LGALS9.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.080316 
Standard error............: 0.040005 
Odds ratio (effect size)..: 0.923 
Lower 95% CI..............: 0.853 
Upper 95% CI..............: 0.998 
Z-value...................: -2.007627 
P-value...................: 0.04468291 
Hosmer and Lemeshow r^2...: 0.0927 
Cox and Snell r^2.........: 0.120479 
Nagelkerke's pseudo r^2...: 0.160715 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.004658 
Standard error............: 0.04711 
Odds ratio (effect size)..: 0.995 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.092 
Z-value...................: -0.098866 
P-value...................: 0.921245 
Hosmer and Lemeshow r^2...: 0.00462 
Cox and Snell r^2.........: 0.004689 
Nagelkerke's pseudo r^2...: 0.007344 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.258815 
Standard error............: 0.048379 
Odds ratio (effect size)..: 1.295 
Lower 95% CI..............: 1.178 
Upper 95% CI..............: 1.424 
Z-value...................: 5.349708 
P-value...................: 8.809647e-08 
Hosmer and Lemeshow r^2...: 0.0766 
Cox and Snell r^2.........: 0.08326 
Nagelkerke's pseudo r^2...: 0.122705 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: IPH 
Effect size...............: 0.029401 
Standard error............: 0.039648 
Odds ratio (effect size)..: 1.03 
Lower 95% CI..............: 0.953 
Upper 95% CI..............: 1.113 
Z-value...................: 0.74157 
P-value...................: 0.4583476 
Hosmer and Lemeshow r^2...: 0.033047 
Cox and Snell r^2.........: 0.043407 
Nagelkerke's pseudo r^2...: 0.058745 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.147841 
Standard error............: 0.039716 
Odds ratio (effect size)..: 1.159 
Lower 95% CI..............: 1.073 
Upper 95% CI..............: 1.253 
Z-value...................: 3.722449 
P-value...................: 0.0001973 
Hosmer and Lemeshow r^2...: 0.034407 
Cox and Snell r^2.........: 0.046105 
Nagelkerke's pseudo r^2...: 0.061773 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.071606 
Standard error............: 0.040673 
Odds ratio (effect size)..: 0.931 
Lower 95% CI..............: 0.86 
Upper 95% CI..............: 1.008 
Z-value...................: -1.760506 
P-value...................: 0.07832213 
Hosmer and Lemeshow r^2...: 0.021624 
Cox and Snell r^2.........: 0.027131 
Nagelkerke's pseudo r^2...: 0.037696 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of ACKR1.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.028424 
Standard error............: 0.047369 
Odds ratio (effect size)..: 0.972 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.067 
Z-value...................: -0.600049 
P-value...................: 0.5484737 
Hosmer and Lemeshow r^2...: 0.089922 
Cox and Snell r^2.........: 0.117089 
Nagelkerke's pseudo r^2...: 0.156192 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.036127 
Standard error............: 0.057121 
Odds ratio (effect size)..: 1.037 
Lower 95% CI..............: 0.927 
Upper 95% CI..............: 1.16 
Z-value...................: 0.632469 
P-value...................: 0.5270804 
Hosmer and Lemeshow r^2...: 0.005036 
Cox and Snell r^2.........: 0.00511 
Nagelkerke's pseudo r^2...: 0.008004 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.240094 
Standard error............: 0.059472 
Odds ratio (effect size)..: 1.271 
Lower 95% CI..............: 1.131 
Upper 95% CI..............: 1.429 
Z-value...................: 4.037113 
P-value...................: 5.411301e-05 
Hosmer and Lemeshow r^2...: 0.064606 
Cox and Snell r^2.........: 0.070697 
Nagelkerke's pseudo r^2...: 0.10419 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: IPH 
Effect size...............: 0.124773 
Standard error............: 0.048924 
Odds ratio (effect size)..: 1.133 
Lower 95% CI..............: 1.029 
Upper 95% CI..............: 1.247 
Z-value...................: 2.550336 
P-value...................: 0.01076191 
Hosmer and Lemeshow r^2...: 0.037934 
Cox and Snell r^2.........: 0.049663 
Nagelkerke's pseudo r^2...: 0.067212 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.15374 
Standard error............: 0.048184 
Odds ratio (effect size)..: 1.166 
Lower 95% CI..............: 1.061 
Upper 95% CI..............: 1.282 
Z-value...................: 3.190681 
P-value...................: 0.001419379 
Hosmer and Lemeshow r^2...: 0.031532 
Cox and Snell r^2.........: 0.042336 
Nagelkerke's pseudo r^2...: 0.056723 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.110602 
Standard error............: 0.048095 
Odds ratio (effect size)..: 0.895 
Lower 95% CI..............: 0.815 
Upper 95% CI..............: 0.984 
Z-value...................: -2.29968 
P-value...................: 0.02146633 
Hosmer and Lemeshow r^2...: 0.02345 
Cox and Snell r^2.........: 0.029388 
Nagelkerke's pseudo r^2...: 0.040832 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of NT5DC2.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.079257 
Standard error............: 0.043824 
Odds ratio (effect size)..: 0.924 
Lower 95% CI..............: 0.848 
Upper 95% CI..............: 1.007 
Z-value...................: -1.808535 
P-value...................: 0.07052335 
Hosmer and Lemeshow r^2...: 0.092121 
Cox and Snell r^2.........: 0.119775 
Nagelkerke's pseudo r^2...: 0.159775 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.001012 
Standard error............: 0.051805 
Odds ratio (effect size)..: 0.999 
Lower 95% CI..............: 0.903 
Upper 95% CI..............: 1.106 
Z-value...................: -0.019535 
P-value...................: 0.9844146 
Hosmer and Lemeshow r^2...: 0.00461 
Cox and Snell r^2.........: 0.004679 
Nagelkerke's pseudo r^2...: 0.007329 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.217611 
Standard error............: 0.050575 
Odds ratio (effect size)..: 1.243 
Lower 95% CI..............: 1.126 
Upper 95% CI..............: 1.373 
Z-value...................: 4.30275 
P-value...................: 1.686913e-05 
Hosmer and Lemeshow r^2...: 0.065972 
Cox and Snell r^2.........: 0.072136 
Nagelkerke's pseudo r^2...: 0.106311 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: IPH 
Effect size...............: 0.066239 
Standard error............: 0.043823 
Odds ratio (effect size)..: 1.068 
Lower 95% CI..............: 0.981 
Upper 95% CI..............: 1.164 
Z-value...................: 1.511511 
P-value...................: 0.1306584 
Hosmer and Lemeshow r^2...: 0.034438 
Cox and Snell r^2.........: 0.045192 
Nagelkerke's pseudo r^2...: 0.061162 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.098625 
Standard error............: 0.043375 
Odds ratio (effect size)..: 1.104 
Lower 95% CI..............: 1.014 
Upper 95% CI..............: 1.202 
Z-value...................: 2.273797 
P-value...................: 0.02297821 
Hosmer and Lemeshow r^2...: 0.027369 
Cox and Snell r^2.........: 0.03685 
Nagelkerke's pseudo r^2...: 0.049373 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.072896 
Standard error............: 0.0449 
Odds ratio (effect size)..: 0.93 
Lower 95% CI..............: 0.851 
Upper 95% CI..............: 1.015 
Z-value...................: -1.623531 
P-value...................: 0.1044759 
Hosmer and Lemeshow r^2...: 0.021235 
Cox and Snell r^2.........: 0.02665 
Nagelkerke's pseudo r^2...: 0.037028 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of TGFBI.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.046886 
Standard error............: 0.039591 
Odds ratio (effect size)..: 0.954 
Lower 95% CI..............: 0.883 
Upper 95% CI..............: 1.031 
Z-value...................: -1.184264 
P-value...................: 0.2363087 
Hosmer and Lemeshow r^2...: 0.090708 
Cox and Snell r^2.........: 0.11805 
Nagelkerke's pseudo r^2...: 0.157475 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.006278 
Standard error............: 0.046823 
Odds ratio (effect size)..: 1.006 
Lower 95% CI..............: 0.918 
Upper 95% CI..............: 1.103 
Z-value...................: 0.134081 
P-value...................: 0.8933385 
Hosmer and Lemeshow r^2...: 0.004629 
Cox and Snell r^2.........: 0.004697 
Nagelkerke's pseudo r^2...: 0.007358 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.220852 
Standard error............: 0.046335 
Odds ratio (effect size)..: 1.247 
Lower 95% CI..............: 1.139 
Upper 95% CI..............: 1.366 
Z-value...................: 4.766361 
P-value...................: 1.875832e-06 
Hosmer and Lemeshow r^2...: 0.070284 
Cox and Snell r^2.........: 0.076666 
Nagelkerke's pseudo r^2...: 0.112987 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: IPH 
Effect size...............: 0.058406 
Standard error............: 0.039456 
Odds ratio (effect size)..: 1.06 
Lower 95% CI..............: 0.981 
Upper 95% CI..............: 1.145 
Z-value...................: 1.480299 
P-value...................: 0.1387935 
Hosmer and Lemeshow r^2...: 0.034364 
Cox and Snell r^2.........: 0.045096 
Nagelkerke's pseudo r^2...: 0.061032 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.103025 
Standard error............: 0.039088 
Odds ratio (effect size)..: 1.109 
Lower 95% CI..............: 1.027 
Upper 95% CI..............: 1.197 
Z-value...................: 2.635706 
P-value...................: 0.008396254 
Hosmer and Lemeshow r^2...: 0.028794 
Cox and Snell r^2.........: 0.038731 
Nagelkerke's pseudo r^2...: 0.051893 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.057712 
Standard error............: 0.040451 
Odds ratio (effect size)..: 0.944 
Lower 95% CI..............: 0.872 
Upper 95% CI..............: 1.022 
Z-value...................: -1.426734 
P-value...................: 0.1536565 
Hosmer and Lemeshow r^2...: 0.020722 
Cox and Snell r^2.........: 0.026015 
Nagelkerke's pseudo r^2...: 0.036145 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of C1QC.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.056028 
Standard error............: 0.036633 
Odds ratio (effect size)..: 0.946 
Lower 95% CI..............: 0.88 
Upper 95% CI..............: 1.016 
Z-value...................: -1.529436 
P-value...................: 0.1261565 
Hosmer and Lemeshow r^2...: 0.091415 
Cox and Snell r^2.........: 0.118913 
Nagelkerke's pseudo r^2...: 0.158626 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.018597 
Standard error............: 0.043323 
Odds ratio (effect size)..: 1.019 
Lower 95% CI..............: 0.936 
Upper 95% CI..............: 1.109 
Z-value...................: 0.42926 
P-value...................: 0.6677338 
Hosmer and Lemeshow r^2...: 0.004804 
Cox and Snell r^2.........: 0.004875 
Nagelkerke's pseudo r^2...: 0.007637 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.252761 
Standard error............: 0.043927 
Odds ratio (effect size)..: 1.288 
Lower 95% CI..............: 1.181 
Upper 95% CI..............: 1.403 
Z-value...................: 5.754151 
P-value...................: 8.707835e-09 
Hosmer and Lemeshow r^2...: 0.080805 
Cox and Snell r^2.........: 0.087625 
Nagelkerke's pseudo r^2...: 0.129138 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: IPH 
Effect size...............: 0.026789 
Standard error............: 0.036426 
Odds ratio (effect size)..: 1.027 
Lower 95% CI..............: 0.956 
Upper 95% CI..............: 1.103 
Z-value...................: 0.735421 
P-value...................: 0.4620831 
Hosmer and Lemeshow r^2...: 0.03304 
Cox and Snell r^2.........: 0.043397 
Nagelkerke's pseudo r^2...: 0.058732 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.131122 
Standard error............: 0.036441 
Odds ratio (effect size)..: 1.14 
Lower 95% CI..............: 1.062 
Upper 95% CI..............: 1.225 
Z-value...................: 3.598226 
P-value...................: 0.0003203952 
Hosmer and Lemeshow r^2...: 0.033646 
Cox and Snell r^2.........: 0.045109 
Nagelkerke's pseudo r^2...: 0.060438 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.064927 
Standard error............: 0.03748 
Odds ratio (effect size)..: 0.937 
Lower 95% CI..............: 0.871 
Upper 95% CI..............: 1.009 
Z-value...................: -1.732302 
P-value...................: 0.08321973 
Hosmer and Lemeshow r^2...: 0.021546 
Cox and Snell r^2.........: 0.027034 
Nagelkerke's pseudo r^2...: 0.037562 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147 

Analysis of S100A9.

- processing CalcificationPlaque

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.064164 
Standard error............: 0.048512 
Odds ratio (effect size)..: 0.938 
Lower 95% CI..............: 0.853 
Upper 95% CI..............: 1.031 
Z-value...................: -1.322639 
P-value...................: 0.1859556 
Hosmer and Lemeshow r^2...: 0.090971 
Cox and Snell r^2.........: 0.118371 
Nagelkerke's pseudo r^2...: 0.157902 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing CollagenPlaque

filter: removed 160 rows (15%), 932 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.03287 
Standard error............: 0.056937 
Odds ratio (effect size)..: 0.968 
Lower 95% CI..............: 0.865 
Upper 95% CI..............: 1.082 
Z-value...................: -0.577303 
P-value...................: 0.563735 
Hosmer and Lemeshow r^2...: 0.00496 
Cox and Snell r^2.........: 0.005033 
Nagelkerke's pseudo r^2...: 0.007883 
Sample size of AE DB......: 1092 
Sample size of model......: 932 
Missing data %............: 14.65202 

- processing Fat10Perc

filter: removed 134 rows (12%), 958 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.321517 
Standard error............: 0.059535 
Odds ratio (effect size)..: 1.379 
Lower 95% CI..............: 1.227 
Upper 95% CI..............: 1.55 
Z-value...................: 5.400502 
P-value...................: 6.64546e-08 
Hosmer and Lemeshow r^2...: 0.077492 
Cox and Snell r^2.........: 0.084188 
Nagelkerke's pseudo r^2...: 0.124073 
Sample size of AE DB......: 1092 
Sample size of model......: 958 
Missing data %............: 12.27106 

- processing IPH

filter: removed 158 rows (14%), 934 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: IPH 
Effect size...............: 0.056738 
Standard error............: 0.048621 
Odds ratio (effect size)..: 1.058 
Lower 95% CI..............: 0.962 
Upper 95% CI..............: 1.164 
Z-value...................: 1.166939 
P-value...................: 0.2432349 
Hosmer and Lemeshow r^2...: 0.0337 
Cox and Snell r^2.........: 0.044244 
Nagelkerke's pseudo r^2...: 0.059879 
Sample size of AE DB......: 1092 
Sample size of model......: 934 
Missing data %............: 14.46886 

- processing MAC_binned

filter: removed 167 rows (15%), 925 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.103522 
Standard error............: 0.048029 
Odds ratio (effect size)..: 1.109 
Lower 95% CI..............: 1.009 
Upper 95% CI..............: 1.219 
Z-value...................: 2.155402 
P-value...................: 0.03113038 
Hosmer and Lemeshow r^2...: 0.02696 
Cox and Snell r^2.........: 0.03631 
Nagelkerke's pseudo r^2...: 0.04865 
Sample size of AE DB......: 1092 
Sample size of model......: 925 
Missing data %............: 15.29304 

- processing SMC_binned

filter: removed 166 rows (15%), 926 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.132651 
Standard error............: 0.049327 
Odds ratio (effect size)..: 0.876 
Lower 95% CI..............: 0.795 
Upper 95% CI..............: 0.965 
Z-value...................: -2.689216 
P-value...................: 0.007162008 
Hosmer and Lemeshow r^2...: 0.025132 
Cox and Snell r^2.........: 0.031463 
Nagelkerke's pseudo r^2...: 0.043715 
Sample size of AE DB......: 1092 
Sample size of model......: 926 
Missing data %............: 15.20147

cat("Edit the column names...\n")

Edit the column names...

colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")

Correct the variable types...

GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

Writing results to Excel-file...

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")

Removing intermediate files...

rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque CONVOCALS_downstream expression levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")

Running linear regression...

for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
              Hypertension.composite + DiabetesStatus + SmokerStatus + 
              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
              MedHx_CVD + stenose, 
              data = currentDF) 
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    # print(model_step)
    # print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}


Analysis of SCGB3A2.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.01824 
Standard error............: 0.049959 
Odds ratio (effect size)..: 1.018 
Lower 95% CI..............: 0.923 
Upper 95% CI..............: 1.123 
T-value...................: 0.365111 
P-value...................: 0.7151329 
R^2.......................: 0.05078 
Adjusted r^2..............: 0.026441 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.151124 
Standard error............: 0.05087 
Odds ratio (effect size)..: 1.163 
Lower 95% CI..............: 1.053 
Upper 95% CI..............: 1.285 
T-value...................: 2.970777 
P-value...................: 0.00306678 
R^2.......................: 0.062091 
Adjusted r^2..............: 0.037977 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.050817 
Standard error............: 0.045928 
Odds ratio (effect size)..: 0.95 
Lower 95% CI..............: 0.869 
Upper 95% CI..............: 1.04 
T-value...................: -1.106458 
P-value...................: 0.2688883 
R^2.......................: 0.05246 
Adjusted r^2..............: 0.028098 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.013465 
Standard error............: 0.051044 
Odds ratio (effect size)..: 0.987 
Lower 95% CI..............: 0.893 
Upper 95% CI..............: 1.09 
T-value...................: -0.263787 
P-value...................: 0.7920224 
R^2.......................: 0.05535 
Adjusted r^2..............: 0.029376 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of LIX1.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.057508 
Standard error............: 0.036086 
Odds ratio (effect size)..: 0.944 
Lower 95% CI..............: 0.88 
Upper 95% CI..............: 1.013 
T-value...................: -1.593636 
P-value...................: 0.111444 
R^2.......................: 0.041538 
Adjusted r^2..............: 0.016962 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.147703 
Standard error............: 0.036551 
Odds ratio (effect size)..: 1.159 
Lower 95% CI..............: 1.079 
Upper 95% CI..............: 1.245 
T-value...................: 4.040975 
P-value...................: 5.880416e-05 
R^2.......................: 0.05964 
Adjusted r^2..............: 0.035463 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.049423 
Standard error............: 0.033143 
Odds ratio (effect size)..: 1.051 
Lower 95% CI..............: 0.985 
Upper 95% CI..............: 1.121 
T-value...................: 1.49121 
P-value...................: 0.136333 
R^2.......................: 0.041744 
Adjusted r^2..............: 0.017107 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.053291 
Standard error............: 0.036716 
Odds ratio (effect size)..: 1.055 
Lower 95% CI..............: 0.982 
Upper 95% CI..............: 1.133 
T-value...................: 1.451435 
P-value...................: 0.1471126 
R^2.......................: 0.037967 
Adjusted r^2..............: 0.011515 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of IGSF9B.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.02591 
Standard error............: 0.049101 
Odds ratio (effect size)..: 1.026 
Lower 95% CI..............: 0.932 
Upper 95% CI..............: 1.13 
T-value...................: 0.527695 
P-value...................: 0.5978692 
R^2.......................: 0.054914 
Adjusted r^2..............: 0.030681 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.152552 
Standard error............: 0.049998 
Odds ratio (effect size)..: 1.165 
Lower 95% CI..............: 1.056 
Upper 95% CI..............: 1.285 
T-value...................: 3.051193 
P-value...................: 0.002360907 
R^2.......................: 0.066651 
Adjusted r^2..............: 0.042654 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.056756 
Standard error............: 0.045144 
Odds ratio (effect size)..: 0.945 
Lower 95% CI..............: 0.865 
Upper 95% CI..............: 1.032 
T-value...................: -1.257216 
P-value...................: 0.2090726 
R^2.......................: 0.05691 
Adjusted r^2..............: 0.032663 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.029292 
Standard error............: 0.049588 
Odds ratio (effect size)..: 0.971 
Lower 95% CI..............: 0.881 
Upper 95% CI..............: 1.07 
T-value...................: -0.590699 
P-value...................: 0.5549154 
R^2.......................: 0.062631 
Adjusted r^2..............: 0.036856 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of ALB.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.019386 
Standard error............: 0.040179 
Odds ratio (effect size)..: 0.981 
Lower 95% CI..............: 0.907 
Upper 95% CI..............: 1.061 
T-value...................: -0.482483 
P-value...................: 0.6296053 
R^2.......................: 0.033345 
Adjusted r^2..............: 0.008559 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.133409 
Standard error............: 0.040856 
Odds ratio (effect size)..: 1.143 
Lower 95% CI..............: 1.055 
Upper 95% CI..............: 1.238 
T-value...................: 3.265348 
P-value...................: 0.001143697 
R^2.......................: 0.046975 
Adjusted r^2..............: 0.022473 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.08426 
Standard error............: 0.036832 
Odds ratio (effect size)..: 0.919 
Lower 95% CI..............: 0.855 
Upper 95% CI..............: 0.988 
T-value...................: -2.287674 
P-value...................: 0.02243821 
R^2.......................: 0.040023 
Adjusted r^2..............: 0.015342 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.062433 
Standard error............: 0.040796 
Odds ratio (effect size)..: 0.939 
Lower 95% CI..............: 0.867 
Upper 95% CI..............: 1.018 
T-value...................: -1.530369 
P-value...................: 0.1263832 
R^2.......................: 0.04605 
Adjusted r^2..............: 0.01982 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of OR10A4.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.035294 
Standard error............: 0.043455 
Odds ratio (effect size)..: 0.965 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.051 
T-value...................: -0.812196 
P-value...................: 0.4169401 
R^2.......................: 0.031923 
Adjusted r^2..............: 0.007101 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.14601 
Standard error............: 0.044201 
Odds ratio (effect size)..: 1.157 
Lower 95% CI..............: 1.061 
Upper 95% CI..............: 1.262 
T-value...................: 3.303339 
P-value...................: 0.001001412 
R^2.......................: 0.045312 
Adjusted r^2..............: 0.020767 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.093079 
Standard error............: 0.039848 
Odds ratio (effect size)..: 0.911 
Lower 95% CI..............: 0.843 
Upper 95% CI..............: 0.985 
T-value...................: -2.335828 
P-value...................: 0.01976676 
R^2.......................: 0.038315 
Adjusted r^2..............: 0.01359 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.058042 
Standard error............: 0.044046 
Odds ratio (effect size)..: 0.944 
Lower 95% CI..............: 0.866 
Upper 95% CI..............: 1.029 
T-value...................: -1.317764 
P-value...................: 0.1880193 
R^2.......................: 0.044637 
Adjusted r^2..............: 0.018368 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of RASEF.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.048369 
Standard error............: 0.041316 
Odds ratio (effect size)..: 1.05 
Lower 95% CI..............: 0.968 
Upper 95% CI..............: 1.138 
T-value...................: 1.170711 
P-value...................: 0.2420913 
R^2.......................: 0.058812 
Adjusted r^2..............: 0.034679 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.131673 
Standard error............: 0.042034 
Odds ratio (effect size)..: 1.141 
Lower 95% CI..............: 1.051 
Upper 95% CI..............: 1.239 
T-value...................: 3.132523 
P-value...................: 0.001801503 
R^2.......................: 0.069729 
Adjusted r^2..............: 0.045812 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.0157 
Standard error............: 0.038002 
Odds ratio (effect size)..: 0.984 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.061 
T-value...................: -0.413144 
P-value...................: 0.6796212 
R^2.......................: 0.057595 
Adjusted r^2..............: 0.033365 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.004294 
Standard error............: 0.042354 
Odds ratio (effect size)..: 0.996 
Lower 95% CI..............: 0.916 
Upper 95% CI..............: 1.082 
T-value...................: -0.101386 
P-value...................: 0.9192735 
R^2.......................: 0.058153 
Adjusted r^2..............: 0.032255 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of NEDD4.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.018894 
Standard error............: 0.03941 
Odds ratio (effect size)..: 1.019 
Lower 95% CI..............: 0.943 
Upper 95% CI..............: 1.101 
T-value...................: 0.479426 
P-value...................: 0.6317769 
R^2.......................: 0.034241 
Adjusted r^2..............: 0.009478 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.122487 
Standard error............: 0.040079 
Odds ratio (effect size)..: 1.13 
Lower 95% CI..............: 1.045 
Upper 95% CI..............: 1.223 
T-value...................: 3.056141 
P-value...................: 0.002322775 
R^2.......................: 0.046253 
Adjusted r^2..............: 0.021732 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.049416 
Standard error............: 0.036182 
Odds ratio (effect size)..: 0.952 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.022 
T-value...................: -1.365765 
P-value...................: 0.1724282 
R^2.......................: 0.03663 
Adjusted r^2..............: 0.011862 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.015192 
Standard error............: 0.040331 
Odds ratio (effect size)..: 0.985 
Lower 95% CI..............: 0.91 
Upper 95% CI..............: 1.066 
T-value...................: -0.376696 
P-value...................: 0.7065151 
R^2.......................: 0.038948 
Adjusted r^2..............: 0.012523 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of TCL1A.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.071498 
Standard error............: 0.033198 
Odds ratio (effect size)..: 0.931 
Lower 95% CI..............: 0.872 
Upper 95% CI..............: 0.994 
T-value...................: -2.15368 
P-value...................: 0.03158714 
R^2.......................: 0.056023 
Adjusted r^2..............: 0.031819 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.113382 
Standard error............: 0.033839 
Odds ratio (effect size)..: 1.12 
Lower 95% CI..............: 1.048 
Upper 95% CI..............: 1.197 
T-value...................: 3.350628 
P-value...................: 0.0008472483 
R^2.......................: 0.06508 
Adjusted r^2..............: 0.041043 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.050361 
Standard error............: 0.03057 
Odds ratio (effect size)..: 1.052 
Lower 95% CI..............: 0.99 
Upper 95% CI..............: 1.117 
T-value...................: 1.647429 
P-value...................: 0.09989498 
R^2.......................: 0.05435 
Adjusted r^2..............: 0.030037 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.049108 
Standard error............: 0.033914 
Odds ratio (effect size)..: 1.05 
Lower 95% CI..............: 0.983 
Upper 95% CI..............: 1.123 
T-value...................: 1.448024 
P-value...................: 0.1480639 
R^2.......................: 0.048764 
Adjusted r^2..............: 0.022609 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of FBXO15.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.012889 
Standard error............: 0.039205 
Odds ratio (effect size)..: 0.987 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.066 
T-value...................: -0.32876 
P-value...................: 0.7424303 
R^2.......................: 0.034828 
Adjusted r^2..............: 0.01008 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.120089 
Standard error............: 0.039911 
Odds ratio (effect size)..: 1.128 
Lower 95% CI..............: 1.043 
Upper 95% CI..............: 1.219 
T-value...................: 3.008946 
P-value...................: 0.002710654 
R^2.......................: 0.046431 
Adjusted r^2..............: 0.021914 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.049341 
Standard error............: 0.036023 
Odds ratio (effect size)..: 0.952 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.021 
T-value...................: -1.369704 
P-value...................: 0.1711954 
R^2.......................: 0.037193 
Adjusted r^2..............: 0.012439 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.037794 
Standard error............: 0.03984 
Odds ratio (effect size)..: 0.963 
Lower 95% CI..............: 0.891 
Upper 95% CI..............: 1.041 
T-value...................: -0.948642 
P-value...................: 0.3431345 
R^2.......................: 0.043421 
Adjusted r^2..............: 0.017119 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of F5.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.001363 
Standard error............: 0.038562 
Odds ratio (effect size)..: 0.999 
Lower 95% CI..............: 0.926 
Upper 95% CI..............: 1.077 
T-value...................: -0.035352 
P-value...................: 0.9718086 
R^2.......................: 0.032737 
Adjusted r^2..............: 0.007936 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.13875 
Standard error............: 0.039155 
Odds ratio (effect size)..: 1.149 
Lower 95% CI..............: 1.064 
Upper 95% CI..............: 1.24 
T-value...................: 3.543665 
P-value...................: 0.0004194143 
R^2.......................: 0.049306 
Adjusted r^2..............: 0.024863 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.045874 
Standard error............: 0.035428 
Odds ratio (effect size)..: 0.955 
Lower 95% CI..............: 0.891 
Upper 95% CI..............: 1.024 
T-value...................: -1.294866 
P-value...................: 0.1957708 
R^2.......................: 0.03534 
Adjusted r^2..............: 0.010538 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.019273 
Standard error............: 0.03946 
Odds ratio (effect size)..: 0.981 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.06 
T-value...................: -0.488425 
P-value...................: 0.6254037 
R^2.......................: 0.039234 
Adjusted r^2..............: 0.012817 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of TMEM212.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.007789 
Standard error............: 0.037762 
Odds ratio (effect size)..: 0.992 
Lower 95% CI..............: 0.921 
Upper 95% CI..............: 1.068 
T-value...................: -0.206266 
P-value...................: 0.8366402 
R^2.......................: 0.036954 
Adjusted r^2..............: 0.01226 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.150955 
Standard error............: 0.038263 
Odds ratio (effect size)..: 1.163 
Lower 95% CI..............: 1.079 
Upper 95% CI..............: 1.254 
T-value...................: 3.945196 
P-value...................: 8.733294e-05 
R^2.......................: 0.056818 
Adjusted r^2..............: 0.032569 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.077944 
Standard error............: 0.034611 
Odds ratio (effect size)..: 0.925 
Lower 95% CI..............: 0.864 
Upper 95% CI..............: 0.99 
T-value...................: -2.251997 
P-value...................: 0.02461524 
R^2.......................: 0.043517 
Adjusted r^2..............: 0.018926 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.038536 
Standard error............: 0.038452 
Odds ratio (effect size)..: 0.962 
Lower 95% CI..............: 0.892 
Upper 95% CI..............: 1.038 
T-value...................: -1.002182 
P-value...................: 0.3166067 
R^2.......................: 0.04492 
Adjusted r^2..............: 0.018658 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of PTPRD.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.025859 
Standard error............: 0.022453 
Odds ratio (effect size)..: 0.974 
Lower 95% CI..............: 0.933 
Upper 95% CI..............: 1.018 
T-value...................: -1.151675 
P-value...................: 0.2498259 
R^2.......................: 0.014949 
Adjusted r^2..............: -0.010309 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.015064 
Standard error............: 0.022968 
Odds ratio (effect size)..: 1.015 
Lower 95% CI..............: 0.97 
Upper 95% CI..............: 1.062 
T-value...................: 0.655888 
P-value...................: 0.5121005 
R^2.......................: 0.014125 
Adjusted r^2..............: -0.011223 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.021021 
Standard error............: 0.020622 
Odds ratio (effect size)..: 0.979 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 1.02 
T-value...................: -1.019359 
P-value...................: 0.3083664 
R^2.......................: 0.014936 
Adjusted r^2..............: -0.010391 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.05165 
Standard error............: 0.022644 
Odds ratio (effect size)..: 0.95 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 0.993 
T-value...................: -2.281002 
P-value...................: 0.02285182 
R^2.......................: 0.024684 
Adjusted r^2..............: -0.002133 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of CYP46A1.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.009996 
Standard error............: 0.039097 
Odds ratio (effect size)..: 0.99 
Lower 95% CI..............: 0.917 
Upper 95% CI..............: 1.069 
T-value...................: -0.255685 
P-value...................: 0.7982649 
R^2.......................: 0.028983 
Adjusted r^2..............: 0.004085 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.130559 
Standard error............: 0.039751 
Odds ratio (effect size)..: 1.139 
Lower 95% CI..............: 1.054 
Upper 95% CI..............: 1.232 
T-value...................: 3.284464 
P-value...................: 0.001069916 
R^2.......................: 0.042645 
Adjusted r^2..............: 0.018031 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.072938 
Standard error............: 0.035865 
Odds ratio (effect size)..: 0.93 
Lower 95% CI..............: 0.867 
Upper 95% CI..............: 0.997 
T-value...................: -2.033677 
P-value...................: 0.04234093 
R^2.......................: 0.034076 
Adjusted r^2..............: 0.009241 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.041506 
Standard error............: 0.03977 
Odds ratio (effect size)..: 0.959 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.037 
T-value...................: -1.043654 
P-value...................: 0.2970106 
R^2.......................: 0.03722 
Adjusted r^2..............: 0.010747 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of OR2T33.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.028732 
Standard error............: 0.040671 
Odds ratio (effect size)..: 1.029 
Lower 95% CI..............: 0.95 
Upper 95% CI..............: 1.115 
T-value...................: 0.706441 
P-value...................: 0.480136 
R^2.......................: 0.038798 
Adjusted r^2..............: 0.014152 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.13532 
Standard error............: 0.041337 
Odds ratio (effect size)..: 1.145 
Lower 95% CI..............: 1.056 
Upper 95% CI..............: 1.242 
T-value...................: 3.273548 
P-value...................: 0.001111489 
R^2.......................: 0.051945 
Adjusted r^2..............: 0.027571 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.071125 
Standard error............: 0.037308 
Odds ratio (effect size)..: 0.931 
Lower 95% CI..............: 0.866 
Upper 95% CI..............: 1.002 
T-value...................: -1.906445 
P-value...................: 0.05698006 
R^2.......................: 0.042905 
Adjusted r^2..............: 0.018298 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.032968 
Standard error............: 0.041528 
Odds ratio (effect size)..: 0.968 
Lower 95% CI..............: 0.892 
Upper 95% CI..............: 1.05 
T-value...................: -0.793866 
P-value...................: 0.4275459 
R^2.......................: 0.043877 
Adjusted r^2..............: 0.017588 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of SORBS2.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.105111 
Standard error............: 0.04155 
Odds ratio (effect size)..: 0.9 
Lower 95% CI..............: 0.83 
Upper 95% CI..............: 0.977 
T-value...................: -2.52974 
P-value...................: 0.01162088 
R^2.......................: 0.038561 
Adjusted r^2..............: 0.013908 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.072964 
Standard error............: 0.042535 
Odds ratio (effect size)..: 1.076 
Lower 95% CI..............: 0.99 
Upper 95% CI..............: 1.169 
T-value...................: 1.715401 
P-value...................: 0.08669099 
R^2.......................: 0.034222 
Adjusted r^2..............: 0.009391 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.046229 
Standard error............: 0.038245 
Odds ratio (effect size)..: 0.955 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.029 
T-value...................: -1.208774 
P-value...................: 0.2271364 
R^2.......................: 0.032289 
Adjusted r^2..............: 0.007409 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.091638 
Standard error............: 0.041774 
Odds ratio (effect size)..: 0.912 
Lower 95% CI..............: 0.841 
Upper 95% CI..............: 0.99 
T-value...................: -2.193683 
P-value...................: 0.02859056 
R^2.......................: 0.04415 
Adjusted r^2..............: 0.017868 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of ITGA7.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.02653 
Standard error............: 0.035164 
Odds ratio (effect size)..: 0.974 
Lower 95% CI..............: 0.909 
Upper 95% CI..............: 1.043 
T-value...................: -0.754454 
P-value...................: 0.4508166 
R^2.......................: 0.03169 
Adjusted r^2..............: 0.006861 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: 0.093754 
Standard error............: 0.035829 
Odds ratio (effect size)..: 1.098 
Lower 95% CI..............: 1.024 
Upper 95% CI..............: 1.178 
T-value...................: 2.616667 
P-value...................: 0.009060743 
R^2.......................: 0.040087 
Adjusted r^2..............: 0.015407 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: -0.074059 
Standard error............: 0.032217 
Odds ratio (effect size)..: 0.929 
Lower 95% CI..............: 0.872 
Upper 95% CI..............: 0.989 
T-value...................: -2.298716 
P-value...................: 0.02179929 
R^2.......................: 0.038071 
Adjusted r^2..............: 0.013339 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.026459 
Standard error............: 0.035643 
Odds ratio (effect size)..: 0.974 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.044 
T-value...................: -0.74235 
P-value...................: 0.4581277 
R^2.......................: 0.03948 
Adjusted r^2..............: 0.013069 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of FOS.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.03993 
Standard error............: 0.068055 
Odds ratio (effect size)..: 0.961 
Lower 95% CI..............: 0.841 
Upper 95% CI..............: 1.098 
T-value...................: -0.586732 
P-value...................: 0.5575626 
R^2.......................: 0.023246 
Adjusted r^2..............: -0.001799 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.217545 
Standard error............: 0.069264 
Odds ratio (effect size)..: 0.804 
Lower 95% CI..............: 0.702 
Upper 95% CI..............: 0.921 
T-value...................: -3.140801 
P-value...................: 0.00175201 
R^2.......................: 0.03529 
Adjusted r^2..............: 0.010487 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.051576 
Standard error............: 0.062602 
Odds ratio (effect size)..: 1.053 
Lower 95% CI..............: 0.931 
Upper 95% CI..............: 1.19 
T-value...................: 0.823874 
P-value...................: 0.410277 
R^2.......................: 0.02331 
Adjusted r^2..............: -0.001801 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.02607 
Standard error............: 0.068276 
Odds ratio (effect size)..: 1.026 
Lower 95% CI..............: 0.898 
Upper 95% CI..............: 1.173 
T-value...................: 0.381833 
P-value...................: 0.7027024 
R^2.......................: 0.033684 
Adjusted r^2..............: 0.007114 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of HMOX1.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.06469 
Standard error............: 0.041905 
Odds ratio (effect size)..: 1.067 
Lower 95% CI..............: 0.983 
Upper 95% CI..............: 1.158 
T-value...................: 1.543706 
P-value...................: 0.1230866 
R^2.......................: 0.039992 
Adjusted r^2..............: 0.015377 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.163204 
Standard error............: 0.042524 
Odds ratio (effect size)..: 0.849 
Lower 95% CI..............: 0.781 
Upper 95% CI..............: 0.923 
T-value...................: -3.837921 
P-value...................: 0.0001346958 
R^2.......................: 0.055489 
Adjusted r^2..............: 0.031205 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.052889 
Standard error............: 0.038527 
Odds ratio (effect size)..: 1.054 
Lower 95% CI..............: 0.978 
Upper 95% CI..............: 1.137 
T-value...................: 1.372781 
P-value...................: 0.170237 
R^2.......................: 0.039113 
Adjusted r^2..............: 0.014408 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.035456 
Standard error............: 0.041713 
Odds ratio (effect size)..: 1.036 
Lower 95% CI..............: 0.955 
Upper 95% CI..............: 1.124 
T-value...................: 0.849993 
P-value...................: 0.3956234 
R^2.......................: 0.041029 
Adjusted r^2..............: 0.014661 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of LAPTM5.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.041377 
Standard error............: 0.08652 
Odds ratio (effect size)..: 1.042 
Lower 95% CI..............: 0.88 
Upper 95% CI..............: 1.235 
T-value...................: 0.478231 
P-value...................: 0.6326267 
R^2.......................: 0.025289 
Adjusted r^2..............: 0.000296 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.320974 
Standard error............: 0.087739 
Odds ratio (effect size)..: 0.725 
Lower 95% CI..............: 0.611 
Upper 95% CI..............: 0.862 
T-value...................: -3.658273 
P-value...................: 0.0002719597 
R^2.......................: 0.042052 
Adjusted r^2..............: 0.017422 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.158023 
Standard error............: 0.079309 
Odds ratio (effect size)..: 1.171 
Lower 95% CI..............: 1.003 
Upper 95% CI..............: 1.368 
T-value...................: 1.992503 
P-value...................: 0.04668334 
R^2.......................: 0.029915 
Adjusted r^2..............: 0.004974 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.047953 
Standard error............: 0.086907 
Odds ratio (effect size)..: 1.049 
Lower 95% CI..............: 0.885 
Upper 95% CI..............: 1.244 
T-value...................: 0.551773 
P-value...................: 0.5812826 
R^2.......................: 0.032019 
Adjusted r^2..............: 0.005403 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of MMP9.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.118267 
Standard error............: 0.080613 
Odds ratio (effect size)..: 1.126 
Lower 95% CI..............: 0.961 
Upper 95% CI..............: 1.318 
T-value...................: 1.467088 
P-value...................: 0.1427765 
R^2.......................: 0.029945 
Adjusted r^2..............: 0.005072 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.303166 
Standard error............: 0.081826 
Odds ratio (effect size)..: 0.738 
Lower 95% CI..............: 0.629 
Upper 95% CI..............: 0.867 
T-value...................: -3.705022 
P-value...................: 0.0002271431 
R^2.......................: 0.044635 
Adjusted r^2..............: 0.020072 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.129022 
Standard error............: 0.074027 
Odds ratio (effect size)..: 1.138 
Lower 95% CI..............: 0.984 
Upper 95% CI..............: 1.315 
T-value...................: 1.742904 
P-value...................: 0.08176632 
R^2.......................: 0.030872 
Adjusted r^2..............: 0.005956 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.05284 
Standard error............: 0.081487 
Odds ratio (effect size)..: 1.054 
Lower 95% CI..............: 0.899 
Upper 95% CI..............: 1.237 
T-value...................: 0.64844 
P-value...................: 0.5169161 
R^2.......................: 0.030768 
Adjusted r^2..............: 0.004117 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of SIGLEC1.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.014046 
Standard error............: 0.07283 
Odds ratio (effect size)..: 1.014 
Lower 95% CI..............: 0.879 
Upper 95% CI..............: 1.17 
T-value...................: 0.192865 
P-value...................: 0.8471173 
R^2.......................: 0.033852 
Adjusted r^2..............: 0.009079 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.252799 
Standard error............: 0.073993 
Odds ratio (effect size)..: 0.777 
Lower 95% CI..............: 0.672 
Upper 95% CI..............: 0.898 
T-value...................: -3.41652 
P-value...................: 0.000668963 
R^2.......................: 0.048153 
Adjusted r^2..............: 0.02368 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.136961 
Standard error............: 0.066797 
Odds ratio (effect size)..: 1.147 
Lower 95% CI..............: 1.006 
Upper 95% CI..............: 1.307 
T-value...................: 2.050394 
P-value...................: 0.04067823 
R^2.......................: 0.038587 
Adjusted r^2..............: 0.013869 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.002539 
Standard error............: 0.073353 
Odds ratio (effect size)..: 0.997 
Lower 95% CI..............: 0.864 
Upper 95% CI..............: 1.152 
T-value...................: -0.034609 
P-value...................: 0.9724015 
R^2.......................: 0.039385 
Adjusted r^2..............: 0.012972 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of FTL.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.011302 
Standard error............: 0.086114 
Odds ratio (effect size)..: 0.989 
Lower 95% CI..............: 0.835 
Upper 95% CI..............: 1.171 
T-value...................: -0.13125 
P-value...................: 0.8956135 
R^2.......................: 0.034579 
Adjusted r^2..............: 0.009825 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.283489 
Standard error............: 0.087526 
Odds ratio (effect size)..: 0.753 
Lower 95% CI..............: 0.634 
Upper 95% CI..............: 0.894 
T-value...................: -3.238919 
P-value...................: 0.001253486 
R^2.......................: 0.047725 
Adjusted r^2..............: 0.023241 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.076982 
Standard error............: 0.079125 
Odds ratio (effect size)..: 1.08 
Lower 95% CI..............: 0.925 
Upper 95% CI..............: 1.261 
T-value...................: 0.97291 
P-value...................: 0.3309165 
R^2.......................: 0.035442 
Adjusted r^2..............: 0.010643 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.053555 
Standard error............: 0.086276 
Odds ratio (effect size)..: 1.055 
Lower 95% CI..............: 0.891 
Upper 95% CI..............: 1.249 
T-value...................: 0.620739 
P-value...................: 0.5349764 
R^2.......................: 0.043256 
Adjusted r^2..............: 0.01695 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of CD14.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.031837 
Standard error............: 0.084314 
Odds ratio (effect size)..: 1.032 
Lower 95% CI..............: 0.875 
Upper 95% CI..............: 1.218 
T-value...................: 0.377601 
P-value...................: 0.7058354 
R^2.......................: 0.03649 
Adjusted r^2..............: 0.011784 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.266677 
Standard error............: 0.085728 
Odds ratio (effect size)..: 0.766 
Lower 95% CI..............: 0.647 
Upper 95% CI..............: 0.906 
T-value...................: -3.110721 
P-value...................: 0.00193807 
R^2.......................: 0.048267 
Adjusted r^2..............: 0.023798 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.112372 
Standard error............: 0.077397 
Odds ratio (effect size)..: 1.119 
Lower 95% CI..............: 0.961 
Upper 95% CI..............: 1.302 
T-value...................: 1.451881 
P-value...................: 0.1469591 
R^2.......................: 0.038548 
Adjusted r^2..............: 0.013828 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.031169 
Standard error............: 0.08498 
Odds ratio (effect size)..: 1.032 
Lower 95% CI..............: 0.873 
Upper 95% CI..............: 1.219 
T-value...................: 0.36678 
P-value...................: 0.7138955 
R^2.......................: 0.042676 
Adjusted r^2..............: 0.016353 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of HCST.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.028344 
Standard error............: 0.082943 
Odds ratio (effect size)..: 1.029 
Lower 95% CI..............: 0.874 
Upper 95% CI..............: 1.21 
T-value...................: 0.34173 
P-value...................: 0.7326508 
R^2.......................: 0.031741 
Adjusted r^2..............: 0.006913 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.252174 
Standard error............: 0.084358 
Odds ratio (effect size)..: 0.777 
Lower 95% CI..............: 0.659 
Upper 95% CI..............: 0.917 
T-value...................: -2.989347 
P-value...................: 0.002888496 
R^2.......................: 0.042661 
Adjusted r^2..............: 0.018047 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.141287 
Standard error............: 0.076053 
Odds ratio (effect size)..: 1.152 
Lower 95% CI..............: 0.992 
Upper 95% CI..............: 1.337 
T-value...................: 1.857742 
P-value...................: 0.06360316 
R^2.......................: 0.035588 
Adjusted r^2..............: 0.010793 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.041802 
Standard error............: 0.083301 
Odds ratio (effect size)..: 1.043 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.228 
T-value...................: 0.501822 
P-value...................: 0.6159523 
R^2.......................: 0.040284 
Adjusted r^2..............: 0.013896 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of TIMP3.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.055712 
Standard error............: 0.07364 
Odds ratio (effect size)..: 1.057 
Lower 95% CI..............: 0.915 
Upper 95% CI..............: 1.221 
T-value...................: 0.756551 
P-value...................: 0.4495591 
R^2.......................: 0.029596 
Adjusted r^2..............: 0.004714 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.153622 
Standard error............: 0.075183 
Odds ratio (effect size)..: 0.858 
Lower 95% CI..............: 0.74 
Upper 95% CI..............: 0.994 
T-value...................: -2.043302 
P-value...................: 0.04137668 
R^2.......................: 0.033846 
Adjusted r^2..............: 0.009006 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.010165 
Standard error............: 0.067722 
Odds ratio (effect size)..: 1.01 
Lower 95% CI..............: 0.885 
Upper 95% CI..............: 1.154 
T-value...................: 0.150096 
P-value...................: 0.8807298 
R^2.......................: 0.028417 
Adjusted r^2..............: 0.003437 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.044011 
Standard error............: 0.074191 
Odds ratio (effect size)..: 0.957 
Lower 95% CI..............: 0.827 
Upper 95% CI..............: 1.107 
T-value...................: -0.593211 
P-value...................: 0.5532342 
R^2.......................: 0.037978 
Adjusted r^2..............: 0.011526 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of CCL2.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.063082 
Standard error............: 0.069264 
Odds ratio (effect size)..: 0.939 
Lower 95% CI..............: 0.82 
Upper 95% CI..............: 1.075 
T-value...................: -0.910748 
P-value...................: 0.3627244 
R^2.......................: 0.039231 
Adjusted r^2..............: 0.014596 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.240472 
Standard error............: 0.070397 
Odds ratio (effect size)..: 0.786 
Lower 95% CI..............: 0.685 
Upper 95% CI..............: 0.903 
T-value...................: -3.415946 
P-value...................: 0.0006703521 
R^2.......................: 0.052601 
Adjusted r^2..............: 0.028243 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.026743 
Standard error............: 0.063723 
Odds ratio (effect size)..: 1.027 
Lower 95% CI..............: 0.907 
Upper 95% CI..............: 1.164 
T-value...................: 0.419671 
P-value...................: 0.6748479 
R^2.......................: 0.037871 
Adjusted r^2..............: 0.013135 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.023538 
Standard error............: 0.069328 
Odds ratio (effect size)..: 1.024 
Lower 95% CI..............: 0.894 
Upper 95% CI..............: 1.173 
T-value...................: 0.339515 
P-value...................: 0.7343253 
R^2.......................: 0.048548 
Adjusted r^2..............: 0.022387 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of SAT1.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.028511 
Standard error............: 0.082367 
Odds ratio (effect size)..: 0.972 
Lower 95% CI..............: 0.827 
Upper 95% CI..............: 1.142 
T-value...................: -0.346142 
P-value...................: 0.729334 
R^2.......................: 0.034477 
Adjusted r^2..............: 0.00972 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.294957 
Standard error............: 0.083582 
Odds ratio (effect size)..: 0.745 
Lower 95% CI..............: 0.632 
Upper 95% CI..............: 0.877 
T-value...................: -3.528964 
P-value...................: 0.0004430043 
R^2.......................: 0.049753 
Adjusted r^2..............: 0.025322 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.064496 
Standard error............: 0.07567 
Odds ratio (effect size)..: 1.067 
Lower 95% CI..............: 0.92 
Upper 95% CI..............: 1.237 
T-value...................: 0.852336 
P-value...................: 0.3943035 
R^2.......................: 0.034689 
Adjusted r^2..............: 0.00987 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.008739 
Standard error............: 0.082802 
Odds ratio (effect size)..: 1.009 
Lower 95% CI..............: 0.858 
Upper 95% CI..............: 1.187 
T-value...................: 0.105538 
P-value...................: 0.9159798 
R^2.......................: 0.040118 
Adjusted r^2..............: 0.013725 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of CD163.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.005373 
Standard error............: 0.054235 
Odds ratio (effect size)..: 1.005 
Lower 95% CI..............: 0.904 
Upper 95% CI..............: 1.118 
T-value...................: 0.099063 
P-value...................: 0.921115 
R^2.......................: 0.027679 
Adjusted r^2..............: 0.002748 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.17021 
Standard error............: 0.0552 
Odds ratio (effect size)..: 0.843 
Lower 95% CI..............: 0.757 
Upper 95% CI..............: 0.94 
T-value...................: -3.083527 
P-value...................: 0.002121755 
R^2.......................: 0.039749 
Adjusted r^2..............: 0.015061 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.036426 
Standard error............: 0.049883 
Odds ratio (effect size)..: 1.037 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 1.144 
T-value...................: 0.730234 
P-value...................: 0.4654786 
R^2.......................: 0.028096 
Adjusted r^2..............: 0.003107 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.004411 
Standard error............: 0.054469 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.903 
Upper 95% CI..............: 1.118 
T-value...................: 0.080984 
P-value...................: 0.9354784 
R^2.......................: 0.029561 
Adjusted r^2..............: 0.002878 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of PTGDS.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.083929 
Standard error............: 0.063062 
Odds ratio (effect size)..: 1.088 
Lower 95% CI..............: 0.961 
Upper 95% CI..............: 1.231 
T-value...................: 1.330896 
P-value...................: 0.1836326 
R^2.......................: 0.020856 
Adjusted r^2..............: -0.004251 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.113253 
Standard error............: 0.064518 
Odds ratio (effect size)..: 0.893 
Lower 95% CI..............: 0.787 
Upper 95% CI..............: 1.013 
T-value...................: -1.755377 
P-value...................: 0.07960909 
R^2.......................: 0.022552 
Adjusted r^2..............: -0.002578 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.0641 
Standard error............: 0.058026 
Odds ratio (effect size)..: 1.066 
Lower 95% CI..............: 0.952 
Upper 95% CI..............: 1.195 
T-value...................: 1.104692 
P-value...................: 0.2696527 
R^2.......................: 0.020095 
Adjusted r^2..............: -0.005099 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.053995 
Standard error............: 0.064087 
Odds ratio (effect size)..: 1.055 
Lower 95% CI..............: 0.931 
Upper 95% CI..............: 1.197 
T-value...................: 0.842529 
P-value...................: 0.3997834 
R^2.......................: 0.02348 
Adjusted r^2..............: -0.003371 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of LGALS9.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.031182 
Standard error............: 0.069531 
Odds ratio (effect size)..: 1.032 
Lower 95% CI..............: 0.9 
Upper 95% CI..............: 1.182 
T-value...................: 0.448464 
P-value...................: 0.6539495 
R^2.......................: 0.030255 
Adjusted r^2..............: 0.00539 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.225993 
Standard error............: 0.070682 
Odds ratio (effect size)..: 0.798 
Lower 95% CI..............: 0.695 
Upper 95% CI..............: 0.916 
T-value...................: -3.197326 
P-value...................: 0.00144617 
R^2.......................: 0.042605 
Adjusted r^2..............: 0.01799 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.086695 
Standard error............: 0.063848 
Odds ratio (effect size)..: 1.091 
Lower 95% CI..............: 0.962 
Upper 95% CI..............: 1.236 
T-value...................: 1.35784 
P-value...................: 0.1749288 
R^2.......................: 0.031776 
Adjusted r^2..............: 0.006883 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.052884 
Standard error............: 0.0697 
Odds ratio (effect size)..: 1.054 
Lower 95% CI..............: 0.92 
Upper 95% CI..............: 1.209 
T-value...................: 0.758733 
P-value...................: 0.448271 
R^2.......................: 0.03601 
Adjusted r^2..............: 0.009504 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of ACKR1.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: 0.044722 
Standard error............: 0.057857 
Odds ratio (effect size)..: 1.046 
Lower 95% CI..............: 0.934 
Upper 95% CI..............: 1.171 
T-value...................: 0.77297 
P-value...................: 0.4397868 
R^2.......................: 0.031052 
Adjusted r^2..............: 0.006207 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.17102 
Standard error............: 0.058953 
Odds ratio (effect size)..: 0.843 
Lower 95% CI..............: 0.751 
Upper 95% CI..............: 0.946 
T-value...................: -2.900938 
P-value...................: 0.003831001 
R^2.......................: 0.040572 
Adjusted r^2..............: 0.015904 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.063841 
Standard error............: 0.053203 
Odds ratio (effect size)..: 1.066 
Lower 95% CI..............: 0.96 
Upper 95% CI..............: 1.183 
T-value...................: 1.19994 
P-value...................: 0.2305471 
R^2.......................: 0.031533 
Adjusted r^2..............: 0.006633 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.089919 
Standard error............: 0.05897 
Odds ratio (effect size)..: 1.094 
Lower 95% CI..............: 0.975 
Upper 95% CI..............: 1.228 
T-value...................: 1.524819 
P-value...................: 0.1277617 
R^2.......................: 0.034641 
Adjusted r^2..............: 0.008097 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of NT5DC2.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.064943 
Standard error............: 0.061926 
Odds ratio (effect size)..: 0.937 
Lower 95% CI..............: 0.83 
Upper 95% CI..............: 1.058 
T-value...................: -1.048715 
P-value...................: 0.2946511 
R^2.......................: 0.027579 
Adjusted r^2..............: 0.002645 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.195296 
Standard error............: 0.063041 
Odds ratio (effect size)..: 0.823 
Lower 95% CI..............: 0.727 
Upper 95% CI..............: 0.931 
T-value...................: -3.097936 
P-value...................: 0.002022525 
R^2.......................: 0.038101 
Adjusted r^2..............: 0.01337 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.038247 
Standard error............: 0.056975 
Odds ratio (effect size)..: 1.039 
Lower 95% CI..............: 0.929 
Upper 95% CI..............: 1.162 
T-value...................: 0.671285 
P-value...................: 0.5022485 
R^2.......................: 0.026203 
Adjusted r^2..............: 0.001166 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.01039 
Standard error............: 0.062344 
Odds ratio (effect size)..: 0.99 
Lower 95% CI..............: 0.876 
Upper 95% CI..............: 1.118 
T-value...................: -0.166648 
P-value...................: 0.8676959 
R^2.......................: 0.033719 
Adjusted r^2..............: 0.00715 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of TGFBI.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.029491 
Standard error............: 0.06934 
Odds ratio (effect size)..: 0.971 
Lower 95% CI..............: 0.848 
Upper 95% CI..............: 1.112 
T-value...................: -0.425313 
P-value...................: 0.6707321 
R^2.......................: 0.036629 
Adjusted r^2..............: 0.011927 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.214208 
Standard error............: 0.070532 
Odds ratio (effect size)..: 0.807 
Lower 95% CI..............: 0.703 
Upper 95% CI..............: 0.927 
T-value...................: -3.037037 
P-value...................: 0.0024732 
R^2.......................: 0.047879 
Adjusted r^2..............: 0.0234 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.032355 
Standard error............: 0.063738 
Odds ratio (effect size)..: 1.033 
Lower 95% CI..............: 0.912 
Upper 95% CI..............: 1.17 
T-value...................: 0.507616 
P-value...................: 0.6118741 
R^2.......................: 0.036331 
Adjusted r^2..............: 0.011555 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: -0.016709 
Standard error............: 0.069733 
Odds ratio (effect size)..: 0.983 
Lower 95% CI..............: 0.858 
Upper 95% CI..............: 1.127 
T-value...................: -0.239617 
P-value...................: 0.8106985 
R^2.......................: 0.041882 
Adjusted r^2..............: 0.015538 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of C1QC.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -2.9e-05 
Standard error............: 0.07489 
Odds ratio (effect size)..: 1 
Lower 95% CI..............: 0.863 
Upper 95% CI..............: 1.158 
T-value...................: -0.000388 
P-value...................: 0.9996906 
R^2.......................: 0.029896 
Adjusted r^2..............: 0.005022 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.258469 
Standard error............: 0.076075 
Odds ratio (effect size)..: 0.772 
Lower 95% CI..............: 0.665 
Upper 95% CI..............: 0.896 
T-value...................: -3.397537 
P-value...................: 0.0007163673 
R^2.......................: 0.044699 
Adjusted r^2..............: 0.020138 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.125203 
Standard error............: 0.068712 
Odds ratio (effect size)..: 1.133 
Lower 95% CI..............: 0.991 
Upper 95% CI..............: 1.297 
T-value...................: 1.822137 
P-value...................: 0.06883827 
R^2.......................: 0.034117 
Adjusted r^2..............: 0.009284 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.023631 
Standard error............: 0.075594 
Odds ratio (effect size)..: 1.024 
Lower 95% CI..............: 0.883 
Upper 95% CI..............: 1.187 
T-value...................: 0.312598 
P-value...................: 0.7546805 
R^2.......................: 0.038868 
Adjusted r^2..............: 0.012441 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011 

Analysis of S100A9.

- processing MAC_rankNorm

filter: removed 331 rows (30%), 761 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' MAC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: MAC_rankNorm 
Effect size...............: -0.0192 
Standard error............: 0.055942 
Odds ratio (effect size)..: 0.981 
Lower 95% CI..............: 0.879 
Upper 95% CI..............: 1.095 
T-value...................: -0.343206 
P-value...................: 0.7315408 
R^2.......................: 0.034931 
Adjusted r^2..............: 0.010186 
Sample size of AE DB......: 1092 
Sample size of model......: 761 
Missing data %............: 30.31135 

- processing SMC_rankNorm

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' SMC_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: SMC_rankNorm 
Effect size...............: -0.225478 
Standard error............: 0.056667 
Odds ratio (effect size)..: 0.798 
Lower 95% CI..............: 0.714 
Upper 95% CI..............: 0.892 
T-value...................: -3.978977 
P-value...................: 7.603263e-05 
R^2.......................: 0.054558 
Adjusted r^2..............: 0.030251 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing MAC_SMC_ratio_rank

filter: removed 333 rows (30%), 759 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' MAC_SMC_ratio_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: MAC_SMC_ratio_rank 
Effect size...............: 0.062081 
Standard error............: 0.051393 
Odds ratio (effect size)..: 1.064 
Lower 95% CI..............: 0.962 
Upper 95% CI..............: 1.177 
T-value...................: 1.207972 
P-value...................: 0.2274443 
R^2.......................: 0.036206 
Adjusted r^2..............: 0.011427 
Sample size of AE DB......: 1092 
Sample size of model......: 759 
Missing data %............: 30.49451 

- processing VesselDensity_rankNorm

filter: removed 381 rows (35%), 711 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' VesselDensity_rankNorm ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: VesselDensity_rankNorm 
Effect size...............: 0.0513 
Standard error............: 0.056437 
Odds ratio (effect size)..: 1.053 
Lower 95% CI..............: 0.942 
Upper 95% CI..............: 1.176 
T-value...................: 0.908981 
P-value...................: 0.3636771 
R^2.......................: 0.03723 
Adjusted r^2..............: 0.010758 
Sample size of AE DB......: 1092 
Sample size of model......: 711 
Missing data %............: 34.89011

cat("Edit the column names...\n")

Edit the column names...

colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")

Correct the variable types...

GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

Writing results to Excel-file...

### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Con.Multi.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")

Removing intermediate files...

rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Binary plaque traits

Analysis of binary plaque traits as a function of plaque CONVOCALS_downstream levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    # print(model_step)
    # print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}


Analysis of SCGB3A2.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.040573 
Standard error............: 0.060931 
Odds ratio (effect size)..: 0.96 
Lower 95% CI..............: 0.852 
Upper 95% CI..............: 1.082 
Z-value...................: -0.665881 
P-value...................: 0.5054871 
Hosmer and Lemeshow r^2...: 0.092272 
Cox and Snell r^2.........: 0.119782 
Nagelkerke's pseudo r^2...: 0.1599 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.056204 
Standard error............: 0.074538 
Odds ratio (effect size)..: 1.058 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.224 
Z-value...................: 0.754034 
P-value...................: 0.4508286 
Hosmer and Lemeshow r^2...: 0.02549 
Cox and Snell r^2.........: 0.025239 
Nagelkerke's pseudo r^2...: 0.039861 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.257181 
Standard error............: 0.06743 
Odds ratio (effect size)..: 0.773 
Lower 95% CI..............: 0.678 
Upper 95% CI..............: 0.882 
Z-value...................: -3.814047 
P-value...................: 0.0001367095 
Hosmer and Lemeshow r^2...: 0.077662 
Cox and Snell r^2.........: 0.08401 
Nagelkerke's pseudo r^2...: 0.124104 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: IPH 
Effect size...............: -0.089708 
Standard error............: 0.060761 
Odds ratio (effect size)..: 0.914 
Lower 95% CI..............: 0.812 
Upper 95% CI..............: 1.03 
Z-value...................: -1.476393 
P-value...................: 0.1398383 
Hosmer and Lemeshow r^2...: 0.04122 
Cox and Snell r^2.........: 0.053924 
Nagelkerke's pseudo r^2...: 0.072929 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.021848 
Standard error............: 0.060559 
Odds ratio (effect size)..: 1.022 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.151 
Z-value...................: 0.360778 
P-value...................: 0.7182653 
Hosmer and Lemeshow r^2...: 0.03756 
Cox and Snell r^2.........: 0.050402 
Nagelkerke's pseudo r^2...: 0.067414 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.182372 
Standard error............: 0.066541 
Odds ratio (effect size)..: 1.2 
Lower 95% CI..............: 1.053 
Upper 95% CI..............: 1.367 
Z-value...................: 2.740743 
P-value...................: 0.006130035 
Hosmer and Lemeshow r^2...: 0.048086 
Cox and Snell r^2.........: 0.059326 
Nagelkerke's pseudo r^2...: 0.082433 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of LIX1.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.013943 
Standard error............: 0.082439 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.839 
Upper 95% CI..............: 1.159 
Z-value...................: -0.169126 
P-value...................: 0.8656974 
Hosmer and Lemeshow r^2...: 0.091913 
Cox and Snell r^2.........: 0.119345 
Nagelkerke's pseudo r^2...: 0.159317 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.181255 
Standard error............: 0.098923 
Odds ratio (effect size)..: 1.199 
Lower 95% CI..............: 0.987 
Upper 95% CI..............: 1.455 
Z-value...................: 1.832284 
P-value...................: 0.0669091 
Hosmer and Lemeshow r^2...: 0.028934 
Cox and Snell r^2.........: 0.0286 
Nagelkerke's pseudo r^2...: 0.045169 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.661877 
Standard error............: 0.099425 
Odds ratio (effect size)..: 0.516 
Lower 95% CI..............: 0.425 
Upper 95% CI..............: 0.627 
Z-value...................: -6.657062 
P-value...................: 2.79356e-11 
Hosmer and Lemeshow r^2...: 0.113624 
Cox and Snell r^2.........: 0.120484 
Nagelkerke's pseudo r^2...: 0.177985 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: IPH 
Effect size...............: -0.292674 
Standard error............: 0.083467 
Odds ratio (effect size)..: 0.746 
Lower 95% CI..............: 0.634 
Upper 95% CI..............: 0.879 
Z-value...................: -3.506448 
P-value...................: 0.0004541306 
Hosmer and Lemeshow r^2...: 0.050718 
Cox and Snell r^2.........: 0.065932 
Nagelkerke's pseudo r^2...: 0.089168 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.299643 
Standard error............: 0.082988 
Odds ratio (effect size)..: 0.741 
Lower 95% CI..............: 0.63 
Upper 95% CI..............: 0.872 
Z-value...................: -3.610695 
P-value...................: 0.000305378 
Hosmer and Lemeshow r^2...: 0.049451 
Cox and Snell r^2.........: 0.065823 
Nagelkerke's pseudo r^2...: 0.088041 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.344556 
Standard error............: 0.087505 
Odds ratio (effect size)..: 1.411 
Lower 95% CI..............: 1.189 
Upper 95% CI..............: 1.675 
Z-value...................: 3.937538 
P-value...................: 8.232178e-05 
Hosmer and Lemeshow r^2...: 0.056024 
Cox and Snell r^2.........: 0.068777 
Nagelkerke's pseudo r^2...: 0.095564 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of IGSF9B.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.010112 
Standard error............: 0.062323 
Odds ratio (effect size)..: 1.01 
Lower 95% CI..............: 0.894 
Upper 95% CI..............: 1.141 
Z-value...................: 0.162252 
P-value...................: 0.8711075 
Hosmer and Lemeshow r^2...: 0.091911 
Cox and Snell r^2.........: 0.119343 
Nagelkerke's pseudo r^2...: 0.159314 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.029323 
Standard error............: 0.076631 
Odds ratio (effect size)..: 1.03 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.197 
Z-value...................: 0.382649 
P-value...................: 0.7019798 
Hosmer and Lemeshow r^2...: 0.024964 
Cox and Snell r^2.........: 0.024725 
Nagelkerke's pseudo r^2...: 0.039049 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.182176 
Standard error............: 0.069098 
Odds ratio (effect size)..: 0.833 
Lower 95% CI..............: 0.728 
Upper 95% CI..............: 0.954 
Z-value...................: -2.636495 
P-value...................: 0.008376733 
Hosmer and Lemeshow r^2...: 0.069581 
Cox and Snell r^2.........: 0.075609 
Nagelkerke's pseudo r^2...: 0.111693 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: IPH 
Effect size...............: -0.058988 
Standard error............: 0.062254 
Odds ratio (effect size)..: 0.943 
Lower 95% CI..............: 0.834 
Upper 95% CI..............: 1.065 
Z-value...................: -0.947532 
P-value...................: 0.3433677 
Hosmer and Lemeshow r^2...: 0.040048 
Cox and Snell r^2.........: 0.052432 
Nagelkerke's pseudo r^2...: 0.07091 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.058723 
Standard error............: 0.061337 
Odds ratio (effect size)..: 0.943 
Lower 95% CI..............: 0.836 
Upper 95% CI..............: 1.063 
Z-value...................: -0.95739 
P-value...................: 0.3383704 
Hosmer and Lemeshow r^2...: 0.038268 
Cox and Snell r^2.........: 0.051328 
Nagelkerke's pseudo r^2...: 0.068653 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.133346 
Standard error............: 0.067082 
Odds ratio (effect size)..: 1.143 
Lower 95% CI..............: 1.002 
Upper 95% CI..............: 1.303 
Z-value...................: 1.987799 
P-value...................: 0.04683393 
Hosmer and Lemeshow r^2...: 0.04443 
Cox and Snell r^2.........: 0.054943 
Nagelkerke's pseudo r^2...: 0.076341 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of ALB.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.083092 
Standard error............: 0.076761 
Odds ratio (effect size)..: 0.92 
Lower 95% CI..............: 0.792 
Upper 95% CI..............: 1.07 
Z-value...................: -1.082473 
P-value...................: 0.2790425 
Hosmer and Lemeshow r^2...: 0.092908 
Cox and Snell r^2.........: 0.120556 
Nagelkerke's pseudo r^2...: 0.160933 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.072409 
Standard error............: 0.09104 
Odds ratio (effect size)..: 1.075 
Lower 95% CI..............: 0.899 
Upper 95% CI..............: 1.285 
Z-value...................: 0.795352 
P-value...................: 0.4264085 
Hosmer and Lemeshow r^2...: 0.025558 
Cox and Snell r^2.........: 0.025305 
Nagelkerke's pseudo r^2...: 0.039966 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.475042 
Standard error............: 0.092646 
Odds ratio (effect size)..: 0.622 
Lower 95% CI..............: 0.519 
Upper 95% CI..............: 0.746 
Z-value...................: -5.127485 
P-value...................: 2.936391e-07 
Hosmer and Lemeshow r^2...: 0.092369 
Cox and Snell r^2.........: 0.099105 
Nagelkerke's pseudo r^2...: 0.146404 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: IPH 
Effect size...............: -0.223566 
Standard error............: 0.07766 
Odds ratio (effect size)..: 0.8 
Lower 95% CI..............: 0.687 
Upper 95% CI..............: 0.931 
Z-value...................: -2.878786 
P-value...................: 0.003992086 
Hosmer and Lemeshow r^2...: 0.046984 
Cox and Snell r^2.........: 0.06123 
Nagelkerke's pseudo r^2...: 0.082809 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.218487 
Standard error............: 0.075932 
Odds ratio (effect size)..: 0.804 
Lower 95% CI..............: 0.693 
Upper 95% CI..............: 0.933 
Z-value...................: -2.877391 
P-value...................: 0.004009785 
Hosmer and Lemeshow r^2...: 0.045062 
Cox and Snell r^2.........: 0.060161 
Nagelkerke's pseudo r^2...: 0.080467 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.118566 
Standard error............: 0.078313 
Odds ratio (effect size)..: 1.126 
Lower 95% CI..............: 0.966 
Upper 95% CI..............: 1.313 
Z-value...................: 1.514002 
P-value...................: 0.1300254 
Hosmer and Lemeshow r^2...: 0.042726 
Cox and Snell r^2.........: 0.052892 
Nagelkerke's pseudo r^2...: 0.073492 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of OR10A4.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.065982 
Standard error............: 0.070298 
Odds ratio (effect size)..: 0.936 
Lower 95% CI..............: 0.816 
Upper 95% CI..............: 1.074 
Z-value...................: -0.938605 
P-value...................: 0.3479336 
Hosmer and Lemeshow r^2...: 0.092654 
Cox and Snell r^2.........: 0.120247 
Nagelkerke's pseudo r^2...: 0.160521 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.076014 
Standard error............: 0.083666 
Odds ratio (effect size)..: 1.079 
Lower 95% CI..............: 0.916 
Upper 95% CI..............: 1.271 
Z-value...................: 0.908541 
P-value...................: 0.3635925 
Hosmer and Lemeshow r^2...: 0.025793 
Cox and Snell r^2.........: 0.025535 
Nagelkerke's pseudo r^2...: 0.040329 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.479302 
Standard error............: 0.086445 
Odds ratio (effect size)..: 0.619 
Lower 95% CI..............: 0.523 
Upper 95% CI..............: 0.734 
Z-value...................: -5.544562 
P-value...................: 2.946915e-08 
Hosmer and Lemeshow r^2...: 0.097786 
Cox and Snell r^2.........: 0.104603 
Nagelkerke's pseudo r^2...: 0.154525 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: IPH 
Effect size...............: -0.213701 
Standard error............: 0.071649 
Odds ratio (effect size)..: 0.808 
Lower 95% CI..............: 0.702 
Upper 95% CI..............: 0.929 
Z-value...................: -2.982612 
P-value...................: 0.002857997 
Hosmer and Lemeshow r^2...: 0.047579 
Cox and Snell r^2.........: 0.061982 
Nagelkerke's pseudo r^2...: 0.083825 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.169529 
Standard error............: 0.069949 
Odds ratio (effect size)..: 0.844 
Lower 95% CI..............: 0.736 
Upper 95% CI..............: 0.968 
Z-value...................: -2.423614 
P-value...................: 0.01536694 
Hosmer and Lemeshow r^2...: 0.042817 
Cox and Snell r^2.........: 0.057251 
Nagelkerke's pseudo r^2...: 0.076576 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.137765 
Standard error............: 0.072117 
Odds ratio (effect size)..: 1.148 
Lower 95% CI..............: 0.996 
Upper 95% CI..............: 1.322 
Z-value...................: 1.910279 
P-value...................: 0.05609724 
Hosmer and Lemeshow r^2...: 0.044053 
Cox and Snell r^2.........: 0.054489 
Nagelkerke's pseudo r^2...: 0.075711 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of RASEF.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.012922 
Standard error............: 0.073434 
Odds ratio (effect size)..: 0.987 
Lower 95% CI..............: 0.855 
Upper 95% CI..............: 1.14 
Z-value...................: -0.175971 
P-value...................: 0.8603164 
Hosmer and Lemeshow r^2...: 0.091915 
Cox and Snell r^2.........: 0.119347 
Nagelkerke's pseudo r^2...: 0.15932 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.055304 
Standard error............: 0.08936 
Odds ratio (effect size)..: 0.946 
Lower 95% CI..............: 0.794 
Upper 95% CI..............: 1.127 
Z-value...................: -0.618886 
P-value...................: 0.5359913 
Hosmer and Lemeshow r^2...: 0.025257 
Cox and Snell r^2.........: 0.025011 
Nagelkerke's pseudo r^2...: 0.039502 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.537988 
Standard error............: 0.095614 
Odds ratio (effect size)..: 0.584 
Lower 95% CI..............: 0.484 
Upper 95% CI..............: 0.704 
Z-value...................: -5.626642 
P-value...................: 1.837514e-08 
Hosmer and Lemeshow r^2...: 0.099211 
Cox and Snell r^2.........: 0.106043 
Nagelkerke's pseudo r^2...: 0.156653 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: IPH 
Effect size...............: -0.125625 
Standard error............: 0.074996 
Odds ratio (effect size)..: 0.882 
Lower 95% CI..............: 0.761 
Upper 95% CI..............: 1.022 
Z-value...................: -1.675103 
P-value...................: 0.09391405 
Hosmer and Lemeshow r^2...: 0.041832 
Cox and Snell r^2.........: 0.054703 
Nagelkerke's pseudo r^2...: 0.073981 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.085215 
Standard error............: 0.073093 
Odds ratio (effect size)..: 0.918 
Lower 95% CI..............: 0.796 
Upper 95% CI..............: 1.06 
Z-value...................: -1.165849 
P-value...................: 0.2436754 
Hosmer and Lemeshow r^2...: 0.038677 
Cox and Snell r^2.........: 0.051861 
Nagelkerke's pseudo r^2...: 0.069366 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.111021 
Standard error............: 0.075658 
Odds ratio (effect size)..: 1.117 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.296 
Z-value...................: 1.467408 
P-value...................: 0.142265 
Hosmer and Lemeshow r^2...: 0.042584 
Cox and Snell r^2.........: 0.052721 
Nagelkerke's pseudo r^2...: 0.073255 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of NEDD4.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.040533 
Standard error............: 0.077545 
Odds ratio (effect size)..: 0.96 
Lower 95% CI..............: 0.825 
Upper 95% CI..............: 1.118 
Z-value...................: -0.522699 
P-value...................: 0.6011835 
Hosmer and Lemeshow r^2...: 0.092125 
Cox and Snell r^2.........: 0.119603 
Nagelkerke's pseudo r^2...: 0.159662 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.021058 
Standard error............: 0.093123 
Odds ratio (effect size)..: 1.021 
Lower 95% CI..............: 0.851 
Upper 95% CI..............: 1.226 
Z-value...................: 0.226134 
P-value...................: 0.8210971 
Hosmer and Lemeshow r^2...: 0.024846 
Cox and Snell r^2.........: 0.024609 
Nagelkerke's pseudo r^2...: 0.038867 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.536358 
Standard error............: 0.099112 
Odds ratio (effect size)..: 0.585 
Lower 95% CI..............: 0.482 
Upper 95% CI..............: 0.71 
Z-value...................: -5.411616 
P-value...................: 6.245851e-08 
Hosmer and Lemeshow r^2...: 0.096353 
Cox and Snell r^2.........: 0.103152 
Nagelkerke's pseudo r^2...: 0.152381 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: IPH 
Effect size...............: -0.202465 
Standard error............: 0.079449 
Odds ratio (effect size)..: 0.817 
Lower 95% CI..............: 0.699 
Upper 95% CI..............: 0.954 
Z-value...................: -2.548355 
P-value...................: 0.01082321 
Hosmer and Lemeshow r^2...: 0.045306 
Cox and Snell r^2.........: 0.05911 
Nagelkerke's pseudo r^2...: 0.079942 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.120324 
Standard error............: 0.076638 
Odds ratio (effect size)..: 0.887 
Lower 95% CI..............: 0.763 
Upper 95% CI..............: 1.03 
Z-value...................: -1.570034 
P-value...................: 0.1164071 
Hosmer and Lemeshow r^2...: 0.039683 
Cox and Snell r^2.........: 0.053174 
Nagelkerke's pseudo r^2...: 0.071122 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.130213 
Standard error............: 0.079455 
Odds ratio (effect size)..: 1.139 
Lower 95% CI..............: 0.975 
Upper 95% CI..............: 1.331 
Z-value...................: 1.638821 
P-value...................: 0.1012505 
Hosmer and Lemeshow r^2...: 0.043106 
Cox and Snell r^2.........: 0.05335 
Nagelkerke's pseudo r^2...: 0.074129 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of TCL1A.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.025051 
Standard error............: 0.09016 
Odds ratio (effect size)..: 0.975 
Lower 95% CI..............: 0.817 
Upper 95% CI..............: 1.164 
Z-value...................: -0.277848 
P-value...................: 0.7811292 
Hosmer and Lemeshow r^2...: 0.091955 
Cox and Snell r^2.........: 0.119396 
Nagelkerke's pseudo r^2...: 0.159385 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.247381 
Standard error............: 0.109164 
Odds ratio (effect size)..: 1.281 
Lower 95% CI..............: 1.034 
Upper 95% CI..............: 1.586 
Z-value...................: 2.266147 
P-value...................: 0.02344237 
Hosmer and Lemeshow r^2...: 0.031169 
Cox and Snell r^2.........: 0.030774 
Nagelkerke's pseudo r^2...: 0.048603 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.705798 
Standard error............: 0.108867 
Odds ratio (effect size)..: 0.494 
Lower 95% CI..............: 0.399 
Upper 95% CI..............: 0.611 
Z-value...................: -6.483135 
P-value...................: 8.983578e-11 
Hosmer and Lemeshow r^2...: 0.110665 
Cox and Snell r^2.........: 0.117539 
Nagelkerke's pseudo r^2...: 0.173634 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: IPH 
Effect size...............: -0.331897 
Standard error............: 0.091298 
Odds ratio (effect size)..: 0.718 
Lower 95% CI..............: 0.6 
Upper 95% CI..............: 0.858 
Z-value...................: -3.635315 
P-value...................: 0.0002776412 
Hosmer and Lemeshow r^2...: 0.051574 
Cox and Snell r^2.........: 0.067007 
Nagelkerke's pseudo r^2...: 0.090622 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.300281 
Standard error............: 0.090265 
Odds ratio (effect size)..: 0.741 
Lower 95% CI..............: 0.621 
Upper 95% CI..............: 0.884 
Z-value...................: -3.326682 
P-value...................: 0.0008788667 
Hosmer and Lemeshow r^2...: 0.047594 
Cox and Snell r^2.........: 0.063432 
Nagelkerke's pseudo r^2...: 0.084842 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.424472 
Standard error............: 0.096915 
Odds ratio (effect size)..: 1.529 
Lower 95% CI..............: 1.264 
Upper 95% CI..............: 1.849 
Z-value...................: 4.379849 
P-value...................: 1.187616e-05 
Hosmer and Lemeshow r^2...: 0.059886 
Cox and Snell r^2.........: 0.073339 
Nagelkerke's pseudo r^2...: 0.101903 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of FBXO15.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.07773 
Standard error............: 0.078145 
Odds ratio (effect size)..: 0.925 
Lower 95% CI..............: 0.794 
Upper 95% CI..............: 1.078 
Z-value...................: -0.994691 
P-value...................: 0.3198866 
Hosmer and Lemeshow r^2...: 0.09275 
Cox and Snell r^2.........: 0.120363 
Nagelkerke's pseudo r^2...: 0.160676 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.005983 
Standard error............: 0.093677 
Odds ratio (effect size)..: 0.994 
Lower 95% CI..............: 0.827 
Upper 95% CI..............: 1.194 
Z-value...................: -0.063863 
P-value...................: 0.949079 
Hosmer and Lemeshow r^2...: 0.024789 
Cox and Snell r^2.........: 0.024553 
Nagelkerke's pseudo r^2...: 0.038778 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.563039 
Standard error............: 0.102858 
Odds ratio (effect size)..: 0.569 
Lower 95% CI..............: 0.466 
Upper 95% CI..............: 0.697 
Z-value...................: -5.473964 
P-value...................: 4.400792e-08 
Hosmer and Lemeshow r^2...: 0.09815 
Cox and Snell r^2.........: 0.104971 
Nagelkerke's pseudo r^2...: 0.155069 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: IPH 
Effect size...............: -0.19042 
Standard error............: 0.080054 
Odds ratio (effect size)..: 0.827 
Lower 95% CI..............: 0.707 
Upper 95% CI..............: 0.967 
Z-value...................: -2.37866 
P-value...................: 0.0173757 
Hosmer and Lemeshow r^2...: 0.044538 
Cox and Snell r^2.........: 0.058137 
Nagelkerke's pseudo r^2...: 0.078626 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.169318 
Standard error............: 0.077775 
Odds ratio (effect size)..: 0.844 
Lower 95% CI..............: 0.725 
Upper 95% CI..............: 0.983 
Z-value...................: -2.177023 
P-value...................: 0.02947883 
Hosmer and Lemeshow r^2...: 0.041792 
Cox and Snell r^2.........: 0.055919 
Nagelkerke's pseudo r^2...: 0.074794 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.140012 
Standard error............: 0.079271 
Odds ratio (effect size)..: 1.15 
Lower 95% CI..............: 0.985 
Upper 95% CI..............: 1.344 
Z-value...................: 1.766243 
P-value...................: 0.07735515 
Hosmer and Lemeshow r^2...: 0.043519 
Cox and Snell r^2.........: 0.053846 
Nagelkerke's pseudo r^2...: 0.074818 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of F5.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.003805 
Standard error............: 0.078945 
Odds ratio (effect size)..: 0.996 
Lower 95% CI..............: 0.853 
Upper 95% CI..............: 1.163 
Z-value...................: -0.048202 
P-value...................: 0.9615549 
Hosmer and Lemeshow r^2...: 0.09189 
Cox and Snell r^2.........: 0.119317 
Nagelkerke's pseudo r^2...: 0.15928 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.051008 
Standard error............: 0.095603 
Odds ratio (effect size)..: 1.052 
Lower 95% CI..............: 0.873 
Upper 95% CI..............: 1.269 
Z-value...................: 0.533542 
P-value...................: 0.5936582 
Hosmer and Lemeshow r^2...: 0.025132 
Cox and Snell r^2.........: 0.024889 
Nagelkerke's pseudo r^2...: 0.039309 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.588088 
Standard error............: 0.100367 
Odds ratio (effect size)..: 0.555 
Lower 95% CI..............: 0.456 
Upper 95% CI..............: 0.676 
Z-value...................: -5.85937 
P-value...................: 4.646264e-09 
Hosmer and Lemeshow r^2...: 0.101928 
Cox and Snell r^2.........: 0.108784 
Nagelkerke's pseudo r^2...: 0.160701 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: IPH 
Effect size...............: -0.167674 
Standard error............: 0.08067 
Odds ratio (effect size)..: 0.846 
Lower 95% CI..............: 0.722 
Upper 95% CI..............: 0.99 
Z-value...................: -2.078507 
P-value...................: 0.03766268 
Hosmer and Lemeshow r^2...: 0.043235 
Cox and Snell r^2.........: 0.056485 
Nagelkerke's pseudo r^2...: 0.076392 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.15605 
Standard error............: 0.079045 
Odds ratio (effect size)..: 0.856 
Lower 95% CI..............: 0.733 
Upper 95% CI..............: 0.999 
Z-value...................: -1.974178 
P-value...................: 0.04836147 
Hosmer and Lemeshow r^2...: 0.040996 
Cox and Snell r^2.........: 0.054885 
Nagelkerke's pseudo r^2...: 0.073411 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.157479 
Standard error............: 0.081887 
Odds ratio (effect size)..: 1.171 
Lower 95% CI..............: 0.997 
Upper 95% CI..............: 1.374 
Z-value...................: 1.923128 
P-value...................: 0.05446402 
Hosmer and Lemeshow r^2...: 0.044104 
Cox and Snell r^2.........: 0.054551 
Nagelkerke's pseudo r^2...: 0.075797 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of TMEM212.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.072707 
Standard error............: 0.081187 
Odds ratio (effect size)..: 0.93 
Lower 95% CI..............: 0.793 
Upper 95% CI..............: 1.09 
Z-value...................: -0.895557 
P-value...................: 0.3704897 
Hosmer and Lemeshow r^2...: 0.092586 
Cox and Snell r^2.........: 0.120164 
Nagelkerke's pseudo r^2...: 0.16041 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.071027 
Standard error............: 0.096433 
Odds ratio (effect size)..: 1.074 
Lower 95% CI..............: 0.889 
Upper 95% CI..............: 1.297 
Z-value...................: 0.736543 
P-value...................: 0.4614002 
Hosmer and Lemeshow r^2...: 0.025446 
Cox and Snell r^2.........: 0.025195 
Nagelkerke's pseudo r^2...: 0.039792 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.618669 
Standard error............: 0.105801 
Odds ratio (effect size)..: 0.539 
Lower 95% CI..............: 0.438 
Upper 95% CI..............: 0.663 
Z-value...................: -5.847456 
P-value...................: 4.991475e-09 
Hosmer and Lemeshow r^2...: 0.103015 
Cox and Snell r^2.........: 0.109878 
Nagelkerke's pseudo r^2...: 0.162317 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: IPH 
Effect size...............: -0.224544 
Standard error............: 0.083234 
Odds ratio (effect size)..: 0.799 
Lower 95% CI..............: 0.679 
Upper 95% CI..............: 0.94 
Z-value...................: -2.697753 
P-value...................: 0.006980919 
Hosmer and Lemeshow r^2...: 0.04607 
Cox and Snell r^2.........: 0.060076 
Nagelkerke's pseudo r^2...: 0.081248 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.154663 
Standard error............: 0.080107 
Odds ratio (effect size)..: 0.857 
Lower 95% CI..............: 0.732 
Upper 95% CI..............: 1.002 
Z-value...................: -1.930701 
P-value...................: 0.05352004 
Hosmer and Lemeshow r^2...: 0.040846 
Cox and Snell r^2.........: 0.054689 
Nagelkerke's pseudo r^2...: 0.073149 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.168928 
Standard error............: 0.082447 
Odds ratio (effect size)..: 1.184 
Lower 95% CI..............: 1.007 
Upper 95% CI..............: 1.392 
Z-value...................: 2.048923 
P-value...................: 0.04046961 
Hosmer and Lemeshow r^2...: 0.044576 
Cox and Snell r^2.........: 0.055118 
Nagelkerke's pseudo r^2...: 0.076586 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of PTPRD.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.089893 
Standard error............: 0.136014 
Odds ratio (effect size)..: 1.094 
Lower 95% CI..............: 0.838 
Upper 95% CI..............: 1.428 
Z-value...................: 0.660908 
P-value...................: 0.5086716 
Hosmer and Lemeshow r^2...: 0.092267 
Cox and Snell r^2.........: 0.119776 
Nagelkerke's pseudo r^2...: 0.159892 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.078759 
Standard error............: 0.162449 
Odds ratio (effect size)..: 0.924 
Lower 95% CI..............: 0.672 
Upper 95% CI..............: 1.271 
Z-value...................: -0.484821 
P-value...................: 0.6278031 
Hosmer and Lemeshow r^2...: 0.025074 
Cox and Snell r^2.........: 0.024832 
Nagelkerke's pseudo r^2...: 0.039219 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.155389 
Standard error............: 0.156329 
Odds ratio (effect size)..: 0.856 
Lower 95% CI..............: 0.63 
Upper 95% CI..............: 1.163 
Z-value...................: -0.993987 
P-value...................: 0.3202291 
Hosmer and Lemeshow r^2...: 0.063368 
Cox and Snell r^2.........: 0.069096 
Nagelkerke's pseudo r^2...: 0.102073 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: IPH 
Effect size...............: -0.195189 
Standard error............: 0.135977 
Odds ratio (effect size)..: 0.823 
Lower 95% CI..............: 0.63 
Upper 95% CI..............: 1.074 
Z-value...................: -1.435464 
P-value...................: 0.151155 
Hosmer and Lemeshow r^2...: 0.041134 
Cox and Snell r^2.........: 0.053816 
Nagelkerke's pseudo r^2...: 0.072782 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.134871 
Standard error............: 0.132968 
Odds ratio (effect size)..: 0.874 
Lower 95% CI..............: 0.673 
Upper 95% CI..............: 1.134 
Z-value...................: -1.014313 
P-value...................: 0.3104337 
Hosmer and Lemeshow r^2...: 0.038375 
Cox and Snell r^2.........: 0.051467 
Nagelkerke's pseudo r^2...: 0.068839 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.210464 
Standard error............: 0.144017 
Odds ratio (effect size)..: 0.81 
Lower 95% CI..............: 0.611 
Upper 95% CI..............: 1.074 
Z-value...................: -1.461383 
P-value...................: 0.1439104 
Hosmer and Lemeshow r^2...: 0.042607 
Cox and Snell r^2.........: 0.052749 
Nagelkerke's pseudo r^2...: 0.073294 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of CYP46A1.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.061118 
Standard error............: 0.078416 
Odds ratio (effect size)..: 0.941 
Lower 95% CI..............: 0.807 
Upper 95% CI..............: 1.097 
Z-value...................: -0.779401 
P-value...................: 0.4357436 
Hosmer and Lemeshow r^2...: 0.092417 
Cox and Snell r^2.........: 0.119958 
Nagelkerke's pseudo r^2...: 0.160136 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.015461 
Standard error............: 0.093743 
Odds ratio (effect size)..: 1.016 
Lower 95% CI..............: 0.845 
Upper 95% CI..............: 1.22 
Z-value...................: 0.164933 
P-value...................: 0.8689968 
Hosmer and Lemeshow r^2...: 0.024817 
Cox and Snell r^2.........: 0.024581 
Nagelkerke's pseudo r^2...: 0.038822 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.573656 
Standard error............: 0.106066 
Odds ratio (effect size)..: 0.563 
Lower 95% CI..............: 0.458 
Upper 95% CI..............: 0.694 
Z-value...................: -5.408469 
P-value...................: 6.35658e-08 
Hosmer and Lemeshow r^2...: 0.097747 
Cox and Snell r^2.........: 0.104563 
Nagelkerke's pseudo r^2...: 0.154466 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: IPH 
Effect size...............: -0.20184 
Standard error............: 0.081126 
Odds ratio (effect size)..: 0.817 
Lower 95% CI..............: 0.697 
Upper 95% CI..............: 0.958 
Z-value...................: -2.487966 
P-value...................: 0.01284759 
Hosmer and Lemeshow r^2...: 0.045073 
Cox and Snell r^2.........: 0.058814 
Nagelkerke's pseudo r^2...: 0.079542 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.181476 
Standard error............: 0.078463 
Odds ratio (effect size)..: 0.834 
Lower 95% CI..............: 0.715 
Upper 95% CI..............: 0.973 
Z-value...................: -2.312878 
P-value...................: 0.02072932 
Hosmer and Lemeshow r^2...: 0.042371 
Cox and Snell r^2.........: 0.056672 
Nagelkerke's pseudo r^2...: 0.075801 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.12165 
Standard error............: 0.079435 
Odds ratio (effect size)..: 1.129 
Lower 95% CI..............: 0.967 
Upper 95% CI..............: 1.32 
Z-value...................: 1.531445 
P-value...................: 0.1256595 
Hosmer and Lemeshow r^2...: 0.042762 
Cox and Snell r^2.........: 0.052936 
Nagelkerke's pseudo r^2...: 0.073553 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of OR2T33.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.030849 
Standard error............: 0.075522 
Odds ratio (effect size)..: 0.97 
Lower 95% CI..............: 0.836 
Upper 95% CI..............: 1.124 
Z-value...................: -0.408473 
P-value...................: 0.6829262 
Hosmer and Lemeshow r^2...: 0.092033 
Cox and Snell r^2.........: 0.119491 
Nagelkerke's pseudo r^2...: 0.159512 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.012687 
Standard error............: 0.090863 
Odds ratio (effect size)..: 1.013 
Lower 95% CI..............: 0.848 
Upper 95% CI..............: 1.21 
Z-value...................: 0.139632 
P-value...................: 0.8889506 
Hosmer and Lemeshow r^2...: 0.024808 
Cox and Snell r^2.........: 0.024571 
Nagelkerke's pseudo r^2...: 0.038807 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.562555 
Standard error............: 0.09902 
Odds ratio (effect size)..: 0.57 
Lower 95% CI..............: 0.469 
Upper 95% CI..............: 0.692 
Z-value...................: -5.681203 
P-value...................: 1.337508e-08 
Hosmer and Lemeshow r^2...: 0.100584 
Cox and Snell r^2.........: 0.107429 
Nagelkerke's pseudo r^2...: 0.1587 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: IPH 
Effect size...............: -0.207202 
Standard error............: 0.077983 
Odds ratio (effect size)..: 0.813 
Lower 95% CI..............: 0.698 
Upper 95% CI..............: 0.947 
Z-value...................: -2.657006 
P-value...................: 0.007883811 
Hosmer and Lemeshow r^2...: 0.045874 
Cox and Snell r^2.........: 0.059828 
Nagelkerke's pseudo r^2...: 0.080912 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.122991 
Standard error............: 0.074825 
Odds ratio (effect size)..: 0.884 
Lower 95% CI..............: 0.764 
Upper 95% CI..............: 1.024 
Z-value...................: -1.643717 
P-value...................: 0.1002346 
Hosmer and Lemeshow r^2...: 0.039903 
Cox and Snell r^2.........: 0.053461 
Nagelkerke's pseudo r^2...: 0.071506 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.120998 
Standard error............: 0.077087 
Odds ratio (effect size)..: 1.129 
Lower 95% CI..............: 0.97 
Upper 95% CI..............: 1.313 
Z-value...................: 1.569624 
P-value...................: 0.1165025 
Hosmer and Lemeshow r^2...: 0.042883 
Cox and Snell r^2.........: 0.053081 
Nagelkerke's pseudo r^2...: 0.073755 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of SORBS2.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.183469 
Standard error............: 0.073484 
Odds ratio (effect size)..: 1.201 
Lower 95% CI..............: 1.04 
Upper 95% CI..............: 1.387 
Z-value...................: 2.49671 
P-value...................: 0.01253515 
Hosmer and Lemeshow r^2...: 0.097348 
Cox and Snell r^2.........: 0.125939 
Nagelkerke's pseudo r^2...: 0.168119 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.072753 
Standard error............: 0.089992 
Odds ratio (effect size)..: 1.075 
Lower 95% CI..............: 0.902 
Upper 95% CI..............: 1.283 
Z-value...................: 0.808445 
P-value...................: 0.4188346 
Hosmer and Lemeshow r^2...: 0.025598 
Cox and Snell r^2.........: 0.025344 
Nagelkerke's pseudo r^2...: 0.040028 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.208031 
Standard error............: 0.079711 
Odds ratio (effect size)..: 0.812 
Lower 95% CI..............: 0.695 
Upper 95% CI..............: 0.95 
Z-value...................: -2.609813 
P-value...................: 0.009059182 
Hosmer and Lemeshow r^2...: 0.069415 
Cox and Snell r^2.........: 0.075435 
Nagelkerke's pseudo r^2...: 0.111437 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: IPH 
Effect size...............: -0.122332 
Standard error............: 0.072003 
Odds ratio (effect size)..: 0.885 
Lower 95% CI..............: 0.768 
Upper 95% CI..............: 1.019 
Z-value...................: -1.698979 
P-value...................: 0.08932309 
Hosmer and Lemeshow r^2...: 0.041864 
Cox and Snell r^2.........: 0.054744 
Nagelkerke's pseudo r^2...: 0.074037 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.299626 
Standard error............: 0.072746 
Odds ratio (effect size)..: 0.741 
Lower 95% CI..............: 0.643 
Upper 95% CI..............: 0.855 
Z-value...................: -4.118766 
P-value...................: 3.809068e-05 
Hosmer and Lemeshow r^2...: 0.05309 
Cox and Snell r^2.........: 0.070493 
Nagelkerke's pseudo r^2...: 0.094286 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.059959 
Standard error............: 0.077058 
Odds ratio (effect size)..: 1.062 
Lower 95% CI..............: 0.913 
Upper 95% CI..............: 1.235 
Z-value...................: 0.77811 
P-value...................: 0.4365039 
Hosmer and Lemeshow r^2...: 0.041085 
Cox and Snell r^2.........: 0.050914 
Nagelkerke's pseudo r^2...: 0.070743 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of ITGA7.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.062161 
Standard error............: 0.087571 
Odds ratio (effect size)..: 0.94 
Lower 95% CI..............: 0.792 
Upper 95% CI..............: 1.116 
Z-value...................: -0.709835 
P-value...................: 0.4778062 
Hosmer and Lemeshow r^2...: 0.092325 
Cox and Snell r^2.........: 0.119847 
Nagelkerke's pseudo r^2...: 0.159987 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.056913 
Standard error............: 0.105308 
Odds ratio (effect size)..: 1.059 
Lower 95% CI..............: 0.861 
Upper 95% CI..............: 1.301 
Z-value...................: 0.540446 
P-value...................: 0.5888892 
Hosmer and Lemeshow r^2...: 0.025142 
Cox and Snell r^2.........: 0.024898 
Nagelkerke's pseudo r^2...: 0.039323 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.478982 
Standard error............: 0.106552 
Odds ratio (effect size)..: 0.619 
Lower 95% CI..............: 0.503 
Upper 95% CI..............: 0.763 
Z-value...................: -4.495281 
P-value...................: 6.947815e-06 
Hosmer and Lemeshow r^2...: 0.084941 
Cox and Snell r^2.........: 0.091513 
Nagelkerke's pseudo r^2...: 0.135188 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: IPH 
Effect size...............: -0.173217 
Standard error............: 0.088534 
Odds ratio (effect size)..: 0.841 
Lower 95% CI..............: 0.707 
Upper 95% CI..............: 1 
Z-value...................: -1.956501 
P-value...................: 0.05040614 
Hosmer and Lemeshow r^2...: 0.04277 
Cox and Snell r^2.........: 0.055895 
Nagelkerke's pseudo r^2...: 0.075594 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: MAC_binned 
Effect size...............: -0.16974 
Standard error............: 0.086628 
Odds ratio (effect size)..: 0.844 
Lower 95% CI..............: 0.712 
Upper 95% CI..............: 1 
Z-value...................: -1.959413 
P-value...................: 0.05006446 
Hosmer and Lemeshow r^2...: 0.040935 
Cox and Snell r^2.........: 0.054805 
Nagelkerke's pseudo r^2...: 0.073304 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: SMC_binned 
Effect size...............: 0.143282 
Standard error............: 0.090472 
Odds ratio (effect size)..: 1.154 
Lower 95% CI..............: 0.967 
Upper 95% CI..............: 1.378 
Z-value...................: 1.58372 
P-value...................: 0.1132575 
Hosmer and Lemeshow r^2...: 0.04294 
Cox and Snell r^2.........: 0.05315 
Nagelkerke's pseudo r^2...: 0.073851 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of FOS.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.024713 
Standard error............: 0.04359 
Odds ratio (effect size)..: 0.976 
Lower 95% CI..............: 0.896 
Upper 95% CI..............: 1.063 
Z-value...................: -0.566942 
P-value...................: 0.5707534 
Hosmer and Lemeshow r^2...: 0.092166 
Cox and Snell r^2.........: 0.119653 
Nagelkerke's pseudo r^2...: 0.159729 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.011045 
Standard error............: 0.052207 
Odds ratio (effect size)..: 0.989 
Lower 95% CI..............: 0.893 
Upper 95% CI..............: 1.096 
Z-value...................: -0.211554 
P-value...................: 0.8324549 
Hosmer and Lemeshow r^2...: 0.024839 
Cox and Snell r^2.........: 0.024602 
Nagelkerke's pseudo r^2...: 0.038855 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.205884 
Standard error............: 0.051538 
Odds ratio (effect size)..: 1.229 
Lower 95% CI..............: 1.111 
Upper 95% CI..............: 1.359 
Z-value...................: 3.994761 
P-value...................: 6.47596e-05 
Hosmer and Lemeshow r^2...: 0.080061 
Cox and Snell r^2.........: 0.08649 
Nagelkerke's pseudo r^2...: 0.127767 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: IPH 
Effect size...............: 0.086698 
Standard error............: 0.043728 
Odds ratio (effect size)..: 1.091 
Lower 95% CI..............: 1.001 
Upper 95% CI..............: 1.188 
Z-value...................: 1.982675 
P-value...................: 0.04740379 
Hosmer and Lemeshow r^2...: 0.042856 
Cox and Snell r^2.........: 0.056005 
Nagelkerke's pseudo r^2...: 0.075742 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.07519 
Standard error............: 0.04316 
Odds ratio (effect size)..: 1.078 
Lower 95% CI..............: 0.991 
Upper 95% CI..............: 1.173 
Z-value...................: 1.742114 
P-value...................: 0.08148858 
Hosmer and Lemeshow r^2...: 0.040191 
Cox and Snell r^2.........: 0.053836 
Nagelkerke's pseudo r^2...: 0.072007 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.087434 
Standard error............: 0.045086 
Odds ratio (effect size)..: 0.916 
Lower 95% CI..............: 0.839 
Upper 95% CI..............: 1.001 
Z-value...................: -1.939273 
P-value...................: 0.05246805 
Hosmer and Lemeshow r^2...: 0.044159 
Cox and Snell r^2.........: 0.054617 
Nagelkerke's pseudo r^2...: 0.075889 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of HMOX1.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.164615 
Standard error............: 0.07168 
Odds ratio (effect size)..: 0.848 
Lower 95% CI..............: 0.737 
Upper 95% CI..............: 0.976 
Z-value...................: -2.296517 
P-value...................: 0.02164633 
Hosmer and Lemeshow r^2...: 0.09653 
Cox and Snell r^2.........: 0.12495 
Nagelkerke's pseudo r^2...: 0.166799 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.077797 
Standard error............: 0.083092 
Odds ratio (effect size)..: 0.925 
Lower 95% CI..............: 0.786 
Upper 95% CI..............: 1.089 
Z-value...................: -0.936281 
P-value...................: 0.3491285 
Hosmer and Lemeshow r^2...: 0.025845 
Cox and Snell r^2.........: 0.025586 
Nagelkerke's pseudo r^2...: 0.040409 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.511461 
Standard error............: 0.096761 
Odds ratio (effect size)..: 1.668 
Lower 95% CI..............: 1.38 
Upper 95% CI..............: 2.016 
Z-value...................: 5.2858 
P-value...................: 1.251569e-07 
Hosmer and Lemeshow r^2...: 0.097297 
Cox and Snell r^2.........: 0.104108 
Nagelkerke's pseudo r^2...: 0.153793 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: IPH 
Effect size...............: 0.214081 
Standard error............: 0.073467 
Odds ratio (effect size)..: 1.239 
Lower 95% CI..............: 1.073 
Upper 95% CI..............: 1.431 
Z-value...................: 2.913963 
P-value...................: 0.00356872 
Hosmer and Lemeshow r^2...: 0.047268 
Cox and Snell r^2.........: 0.061588 
Nagelkerke's pseudo r^2...: 0.083293 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.257879 
Standard error............: 0.072969 
Odds ratio (effect size)..: 1.294 
Lower 95% CI..............: 1.122 
Upper 95% CI..............: 1.493 
Z-value...................: 3.534108 
P-value...................: 0.0004091543 
Hosmer and Lemeshow r^2...: 0.049163 
Cox and Snell r^2.........: 0.065453 
Nagelkerke's pseudo r^2...: 0.087546 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.171662 
Standard error............: 0.07197 
Odds ratio (effect size)..: 0.842 
Lower 95% CI..............: 0.731 
Upper 95% CI..............: 0.97 
Z-value...................: -2.385184 
P-value...................: 0.01707058 
Hosmer and Lemeshow r^2...: 0.046021 
Cox and Snell r^2.........: 0.056853 
Nagelkerke's pseudo r^2...: 0.078996 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of LAPTM5.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.06623 
Standard error............: 0.034933 
Odds ratio (effect size)..: 0.936 
Lower 95% CI..............: 0.874 
Upper 95% CI..............: 1.002 
Z-value...................: -1.895903 
P-value...................: 0.05797286 
Hosmer and Lemeshow r^2...: 0.095007 
Cox and Snell r^2.........: 0.123105 
Nagelkerke's pseudo r^2...: 0.164336 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.000468 
Standard error............: 0.042046 
Odds ratio (effect size)..: 1 
Lower 95% CI..............: 0.92 
Upper 95% CI..............: 1.085 
Z-value...................: -0.011142 
P-value...................: 0.9911099 
Hosmer and Lemeshow r^2...: 0.024784 
Cox and Snell r^2.........: 0.024548 
Nagelkerke's pseudo r^2...: 0.03877 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.226285 
Standard error............: 0.042073 
Odds ratio (effect size)..: 1.254 
Lower 95% CI..............: 1.155 
Upper 95% CI..............: 1.362 
Z-value...................: 5.378412 
P-value...................: 7.514553e-08 
Hosmer and Lemeshow r^2...: 0.094928 
Cox and Snell r^2.........: 0.101707 
Nagelkerke's pseudo r^2...: 0.150246 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: IPH 
Effect size...............: 0.018337 
Standard error............: 0.034774 
Odds ratio (effect size)..: 1.019 
Lower 95% CI..............: 0.951 
Upper 95% CI..............: 1.09 
Z-value...................: 0.527325 
P-value...................: 0.5979681 
Hosmer and Lemeshow r^2...: 0.039483 
Cox and Snell r^2.........: 0.051712 
Nagelkerke's pseudo r^2...: 0.069937 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.101956 
Standard error............: 0.03459 
Odds ratio (effect size)..: 1.107 
Lower 95% CI..............: 1.035 
Upper 95% CI..............: 1.185 
Z-value...................: 2.947597 
P-value...................: 0.003202544 
Hosmer and Lemeshow r^2...: 0.045362 
Cox and Snell r^2.........: 0.06055 
Nagelkerke's pseudo r^2...: 0.080987 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.044504 
Standard error............: 0.036075 
Odds ratio (effect size)..: 0.956 
Lower 95% CI..............: 0.891 
Upper 95% CI..............: 1.027 
Z-value...................: -1.233658 
P-value...................: 0.2173304 
Hosmer and Lemeshow r^2...: 0.041977 
Cox and Snell r^2.........: 0.051989 
Nagelkerke's pseudo r^2...: 0.072237 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of MMP9.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.080827 
Standard error............: 0.037261 
Odds ratio (effect size)..: 0.922 
Lower 95% CI..............: 0.857 
Upper 95% CI..............: 0.992 
Z-value...................: -2.169235 
P-value...................: 0.03006488 
Hosmer and Lemeshow r^2...: 0.095981 
Cox and Snell r^2.........: 0.124285 
Nagelkerke's pseudo r^2...: 0.165911 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.020816 
Standard error............: 0.044476 
Odds ratio (effect size)..: 0.979 
Lower 95% CI..............: 0.898 
Upper 95% CI..............: 1.069 
Z-value...................: -0.468016 
P-value...................: 0.6397733 
Hosmer and Lemeshow r^2...: 0.025052 
Cox and Snell r^2.........: 0.02481 
Nagelkerke's pseudo r^2...: 0.039184 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.25834 
Standard error............: 0.046691 
Odds ratio (effect size)..: 1.295 
Lower 95% CI..............: 1.182 
Upper 95% CI..............: 1.419 
Z-value...................: 5.532917 
P-value...................: 3.149494e-08 
Hosmer and Lemeshow r^2...: 0.098153 
Cox and Snell r^2.........: 0.104974 
Nagelkerke's pseudo r^2...: 0.155073 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: IPH 
Effect size...............: 0.009727 
Standard error............: 0.036934 
Odds ratio (effect size)..: 1.01 
Lower 95% CI..............: 0.939 
Upper 95% CI..............: 1.086 
Z-value...................: 0.263368 
P-value...................: 0.7922669 
Hosmer and Lemeshow r^2...: 0.039292 
Cox and Snell r^2.........: 0.051469 
Nagelkerke's pseudo r^2...: 0.069608 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.133399 
Standard error............: 0.037274 
Odds ratio (effect size)..: 1.143 
Lower 95% CI..............: 1.062 
Upper 95% CI..............: 1.229 
Z-value...................: 3.578891 
P-value...................: 0.0003450555 
Hosmer and Lemeshow r^2...: 0.049242 
Cox and Snell r^2.........: 0.065555 
Nagelkerke's pseudo r^2...: 0.087682 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.088211 
Standard error............: 0.038228 
Odds ratio (effect size)..: 0.916 
Lower 95% CI..............: 0.849 
Upper 95% CI..............: 0.987 
Z-value...................: -2.307497 
P-value...................: 0.02102714 
Hosmer and Lemeshow r^2...: 0.045678 
Cox and Snell r^2.........: 0.056441 
Nagelkerke's pseudo r^2...: 0.078423 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of SIGLEC1.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.051634 
Standard error............: 0.041703 
Odds ratio (effect size)..: 0.95 
Lower 95% CI..............: 0.875 
Upper 95% CI..............: 1.031 
Z-value...................: -1.238143 
P-value...................: 0.2156629 
Hosmer and Lemeshow r^2...: 0.093215 
Cox and Snell r^2.........: 0.12093 
Nagelkerke's pseudo r^2...: 0.161433 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.00133 
Standard error............: 0.049876 
Odds ratio (effect size)..: 1.001 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.104 
Z-value...................: 0.02666 
P-value...................: 0.9787313 
Hosmer and Lemeshow r^2...: 0.024785 
Cox and Snell r^2.........: 0.024549 
Nagelkerke's pseudo r^2...: 0.038771 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.261883 
Standard error............: 0.050957 
Odds ratio (effect size)..: 1.299 
Lower 95% CI..............: 1.176 
Upper 95% CI..............: 1.436 
Z-value...................: 5.139242 
P-value...................: 2.758492e-07 
Hosmer and Lemeshow r^2...: 0.092271 
Cox and Snell r^2.........: 0.099006 
Nagelkerke's pseudo r^2...: 0.146257 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: IPH 
Effect size...............: 0.043778 
Standard error............: 0.041634 
Odds ratio (effect size)..: 1.045 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.134 
Z-value...................: 1.051506 
P-value...................: 0.2930264 
Hosmer and Lemeshow r^2...: 0.040243 
Cox and Snell r^2.........: 0.052681 
Nagelkerke's pseudo r^2...: 0.071247 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.095764 
Standard error............: 0.041309 
Odds ratio (effect size)..: 1.1 
Lower 95% CI..............: 1.015 
Upper 95% CI..............: 1.193 
Z-value...................: 2.318201 
P-value...................: 0.0204384 
Hosmer and Lemeshow r^2...: 0.042329 
Cox and Snell r^2.........: 0.056617 
Nagelkerke's pseudo r^2...: 0.075728 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.078646 
Standard error............: 0.042925 
Odds ratio (effect size)..: 0.924 
Lower 95% CI..............: 0.85 
Upper 95% CI..............: 1.006 
Z-value...................: -1.832184 
P-value...................: 0.06692402 
Hosmer and Lemeshow r^2...: 0.043768 
Cox and Snell r^2.........: 0.054147 
Nagelkerke's pseudo r^2...: 0.075236 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of FTL.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.064721 
Standard error............: 0.035281 
Odds ratio (effect size)..: 0.937 
Lower 95% CI..............: 0.875 
Upper 95% CI..............: 1.004 
Z-value...................: -1.834426 
P-value...................: 0.06659071 
Hosmer and Lemeshow r^2...: 0.094811 
Cox and Snell r^2.........: 0.122866 
Nagelkerke's pseudo r^2...: 0.164018 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.016638 
Standard error............: 0.042571 
Odds ratio (effect size)..: 1.017 
Lower 95% CI..............: 0.935 
Upper 95% CI..............: 1.105 
Z-value...................: 0.390843 
P-value...................: 0.6959132 
Hosmer and Lemeshow r^2...: 0.024971 
Cox and Snell r^2.........: 0.024731 
Nagelkerke's pseudo r^2...: 0.039059 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.169431 
Standard error............: 0.040085 
Odds ratio (effect size)..: 1.185 
Lower 95% CI..............: 1.095 
Upper 95% CI..............: 1.281 
Z-value...................: 4.22682 
P-value...................: 2.370171e-05 
Hosmer and Lemeshow r^2...: 0.081517 
Cox and Snell r^2.........: 0.087991 
Nagelkerke's pseudo r^2...: 0.129985 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: IPH 
Effect size...............: 0.006777 
Standard error............: 0.035161 
Odds ratio (effect size)..: 1.007 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 1.079 
Z-value...................: 0.192755 
P-value...................: 0.8471505 
Hosmer and Lemeshow r^2...: 0.039262 
Cox and Snell r^2.........: 0.051431 
Nagelkerke's pseudo r^2...: 0.069557 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.077392 
Standard error............: 0.034731 
Odds ratio (effect size)..: 1.08 
Lower 95% CI..............: 1.009 
Upper 95% CI..............: 1.157 
Z-value...................: 2.22834 
P-value...................: 0.02585783 
Hosmer and Lemeshow r^2...: 0.041944 
Cox and Snell r^2.........: 0.056118 
Nagelkerke's pseudo r^2...: 0.075059 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.044049 
Standard error............: 0.036819 
Odds ratio (effect size)..: 0.957 
Lower 95% CI..............: 0.89 
Upper 95% CI..............: 1.029 
Z-value...................: -1.196363 
P-value...................: 0.2315549 
Hosmer and Lemeshow r^2...: 0.041894 
Cox and Snell r^2.........: 0.05189 
Nagelkerke's pseudo r^2...: 0.0721 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of CD14.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.064237 
Standard error............: 0.035757 
Odds ratio (effect size)..: 0.938 
Lower 95% CI..............: 0.874 
Upper 95% CI..............: 1.006 
Z-value...................: -1.796473 
P-value...................: 0.07241925 
Hosmer and Lemeshow r^2...: 0.094688 
Cox and Snell r^2.........: 0.122717 
Nagelkerke's pseudo r^2...: 0.163819 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.012369 
Standard error............: 0.04303 
Odds ratio (effect size)..: 1.012 
Lower 95% CI..............: 0.931 
Upper 95% CI..............: 1.102 
Z-value...................: 0.287449 
P-value...................: 0.7737684 
Hosmer and Lemeshow r^2...: 0.024885 
Cox and Snell r^2.........: 0.024647 
Nagelkerke's pseudo r^2...: 0.038927 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.223993 
Standard error............: 0.042627 
Odds ratio (effect size)..: 1.251 
Lower 95% CI..............: 1.151 
Upper 95% CI..............: 1.36 
Z-value...................: 5.254699 
P-value...................: 1.482672e-07 
Hosmer and Lemeshow r^2...: 0.093294 
Cox and Snell r^2.........: 0.100047 
Nagelkerke's pseudo r^2...: 0.147794 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: IPH 
Effect size...............: 0.024076 
Standard error............: 0.035562 
Odds ratio (effect size)..: 1.024 
Lower 95% CI..............: 0.955 
Upper 95% CI..............: 1.098 
Z-value...................: 0.677011 
P-value...................: 0.4983987 
Hosmer and Lemeshow r^2...: 0.039648 
Cox and Snell r^2.........: 0.051923 
Nagelkerke's pseudo r^2...: 0.070222 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.092113 
Standard error............: 0.035363 
Odds ratio (effect size)..: 1.096 
Lower 95% CI..............: 1.023 
Upper 95% CI..............: 1.175 
Z-value...................: 2.60475 
P-value...................: 0.009194121 
Hosmer and Lemeshow r^2...: 0.043615 
Cox and Snell r^2.........: 0.058286 
Nagelkerke's pseudo r^2...: 0.077959 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.042279 
Standard error............: 0.036901 
Odds ratio (effect size)..: 0.959 
Lower 95% CI..............: 0.892 
Upper 95% CI..............: 1.031 
Z-value...................: -1.145749 
P-value...................: 0.2518988 
Hosmer and Lemeshow r^2...: 0.041772 
Cox and Snell r^2.........: 0.051742 
Nagelkerke's pseudo r^2...: 0.071895 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of HCST.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.075622 
Standard error............: 0.036559 
Odds ratio (effect size)..: 0.927 
Lower 95% CI..............: 0.863 
Upper 95% CI..............: 0.996 
Z-value...................: -2.068504 
P-value...................: 0.03859261 
Hosmer and Lemeshow r^2...: 0.095601 
Cox and Snell r^2.........: 0.123825 
Nagelkerke's pseudo r^2...: 0.165297 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.018064 
Standard error............: 0.044066 
Odds ratio (effect size)..: 1.018 
Lower 95% CI..............: 0.934 
Upper 95% CI..............: 1.11 
Z-value...................: 0.409926 
P-value...................: 0.6818604 
Hosmer and Lemeshow r^2...: 0.02499 
Cox and Snell r^2.........: 0.02475 
Nagelkerke's pseudo r^2...: 0.039089 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.194142 
Standard error............: 0.042794 
Odds ratio (effect size)..: 1.214 
Lower 95% CI..............: 1.117 
Upper 95% CI..............: 1.321 
Z-value...................: 4.536696 
P-value...................: 5.714233e-06 
Hosmer and Lemeshow r^2...: 0.085017 
Cox and Snell r^2.........: 0.091591 
Nagelkerke's pseudo r^2...: 0.135302 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: IPH 
Effect size...............: 0.013028 
Standard error............: 0.036353 
Odds ratio (effect size)..: 1.013 
Lower 95% CI..............: 0.943 
Upper 95% CI..............: 1.088 
Z-value...................: 0.358391 
P-value...................: 0.7200507 
Hosmer and Lemeshow r^2...: 0.039346 
Cox and Snell r^2.........: 0.051538 
Nagelkerke's pseudo r^2...: 0.069701 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.099137 
Standard error............: 0.036122 
Odds ratio (effect size)..: 1.104 
Lower 95% CI..............: 1.029 
Upper 95% CI..............: 1.185 
Z-value...................: 2.744468 
P-value...................: 0.0060609 
Hosmer and Lemeshow r^2...: 0.044303 
Cox and Snell r^2.........: 0.059178 
Nagelkerke's pseudo r^2...: 0.079152 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.035286 
Standard error............: 0.037741 
Odds ratio (effect size)..: 0.965 
Lower 95% CI..............: 0.896 
Upper 95% CI..............: 1.039 
Z-value...................: -0.934948 
P-value...................: 0.3498151 
Hosmer and Lemeshow r^2...: 0.041344 
Cox and Snell r^2.........: 0.051225 
Nagelkerke's pseudo r^2...: 0.071176 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of TIMP3.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.017939 
Standard error............: 0.040264 
Odds ratio (effect size)..: 0.982 
Lower 95% CI..............: 0.908 
Upper 95% CI..............: 1.063 
Z-value...................: -0.445539 
P-value...................: 0.6559305 
Hosmer and Lemeshow r^2...: 0.09206 
Cox and Snell r^2.........: 0.119524 
Nagelkerke's pseudo r^2...: 0.159556 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.008885 
Standard error............: 0.048146 
Odds ratio (effect size)..: 0.991 
Lower 95% CI..............: 0.902 
Upper 95% CI..............: 1.089 
Z-value...................: -0.18455 
P-value...................: 0.8535824 
Hosmer and Lemeshow r^2...: 0.024825 
Cox and Snell r^2.........: 0.024589 
Nagelkerke's pseudo r^2...: 0.038835 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.21185 
Standard error............: 0.04867 
Odds ratio (effect size)..: 1.236 
Lower 95% CI..............: 1.124 
Upper 95% CI..............: 1.36 
Z-value...................: 4.352813 
P-value...................: 1.344018e-05 
Hosmer and Lemeshow r^2...: 0.083627 
Cox and Snell r^2.........: 0.090163 
Nagelkerke's pseudo r^2...: 0.133194 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: IPH 
Effect size...............: 0.061699 
Standard error............: 0.040468 
Odds ratio (effect size)..: 1.064 
Lower 95% CI..............: 0.983 
Upper 95% CI..............: 1.151 
Z-value...................: 1.524646 
P-value...................: 0.1273473 
Hosmer and Lemeshow r^2...: 0.04137 
Cox and Snell r^2.........: 0.054116 
Nagelkerke's pseudo r^2...: 0.073188 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.105278 
Standard error............: 0.040398 
Odds ratio (effect size)..: 1.111 
Lower 95% CI..............: 1.026 
Upper 95% CI..............: 1.203 
Z-value...................: 2.60601 
P-value...................: 0.009160375 
Hosmer and Lemeshow r^2...: 0.043654 
Cox and Snell r^2.........: 0.058337 
Nagelkerke's pseudo r^2...: 0.078028 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.08199 
Standard error............: 0.041479 
Odds ratio (effect size)..: 0.921 
Lower 95% CI..............: 0.849 
Upper 95% CI..............: 0.999 
Z-value...................: -1.97667 
P-value...................: 0.0480789 
Hosmer and Lemeshow r^2...: 0.044302 
Cox and Snell r^2.........: 0.054789 
Nagelkerke's pseudo r^2...: 0.076128 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of CCL2.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.056922 
Standard error............: 0.043301 
Odds ratio (effect size)..: 0.945 
Lower 95% CI..............: 0.868 
Upper 95% CI..............: 1.028 
Z-value...................: -1.314555 
P-value...................: 0.1886596 
Hosmer and Lemeshow r^2...: 0.093385 
Cox and Snell r^2.........: 0.121136 
Nagelkerke's pseudo r^2...: 0.161708 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.027636 
Standard error............: 0.05204 
Odds ratio (effect size)..: 0.973 
Lower 95% CI..............: 0.878 
Upper 95% CI..............: 1.077 
Z-value...................: -0.531063 
P-value...................: 0.5953754 
Hosmer and Lemeshow r^2...: 0.025129 
Cox and Snell r^2.........: 0.024886 
Nagelkerke's pseudo r^2...: 0.039304 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.254137 
Standard error............: 0.052096 
Odds ratio (effect size)..: 1.289 
Lower 95% CI..............: 1.164 
Upper 95% CI..............: 1.428 
Z-value...................: 4.87822 
P-value...................: 1.070476e-06 
Hosmer and Lemeshow r^2...: 0.089098 
Cox and Snell r^2.........: 0.09577 
Nagelkerke's pseudo r^2...: 0.141476 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: IPH 
Effect size...............: 0.046406 
Standard error............: 0.043308 
Odds ratio (effect size)..: 1.047 
Lower 95% CI..............: 0.962 
Upper 95% CI..............: 1.14 
Z-value...................: 1.071533 
P-value...................: 0.2839298 
Hosmer and Lemeshow r^2...: 0.040282 
Cox and Snell r^2.........: 0.05273 
Nagelkerke's pseudo r^2...: 0.071314 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.039803 
Standard error............: 0.042608 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.957 
Upper 95% CI..............: 1.131 
Z-value...................: 0.934162 
P-value...................: 0.3502201 
Hosmer and Lemeshow r^2...: 0.03823 
Cox and Snell r^2.........: 0.051278 
Nagelkerke's pseudo r^2...: 0.068586 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.060656 
Standard error............: 0.044894 
Odds ratio (effect size)..: 0.941 
Lower 95% CI..............: 0.862 
Upper 95% CI..............: 1.028 
Z-value...................: -1.351104 
P-value...................: 0.176662 
Hosmer and Lemeshow r^2...: 0.04227 
Cox and Snell r^2.........: 0.052343 
Nagelkerke's pseudo r^2...: 0.07273 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of SAT1.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.056716 
Standard error............: 0.036807 
Odds ratio (effect size)..: 0.945 
Lower 95% CI..............: 0.879 
Upper 95% CI..............: 1.016 
Z-value...................: -1.54092 
P-value...................: 0.1233363 
Hosmer and Lemeshow r^2...: 0.093946 
Cox and Snell r^2.........: 0.121817 
Nagelkerke's pseudo r^2...: 0.162617 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.013124 
Standard error............: 0.04452 
Odds ratio (effect size)..: 1.013 
Lower 95% CI..............: 0.929 
Upper 95% CI..............: 1.106 
Z-value...................: 0.294786 
P-value...................: 0.7681576 
Hosmer and Lemeshow r^2...: 0.02489 
Cox and Snell r^2.........: 0.024652 
Nagelkerke's pseudo r^2...: 0.038935 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.194755 
Standard error............: 0.043615 
Odds ratio (effect size)..: 1.215 
Lower 95% CI..............: 1.115 
Upper 95% CI..............: 1.323 
Z-value...................: 4.465302 
P-value...................: 7.995597e-06 
Hosmer and Lemeshow r^2...: 0.084293 
Cox and Snell r^2.........: 0.090848 
Nagelkerke's pseudo r^2...: 0.134205 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: IPH 
Effect size...............: 0.035008 
Standard error............: 0.036888 
Odds ratio (effect size)..: 1.036 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.113 
Z-value...................: 0.949038 
P-value...................: 0.3426015 
Hosmer and Lemeshow r^2...: 0.040054 
Cox and Snell r^2.........: 0.05244 
Nagelkerke's pseudo r^2...: 0.070921 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.081074 
Standard error............: 0.036443 
Odds ratio (effect size)..: 1.084 
Lower 95% CI..............: 1.01 
Upper 95% CI..............: 1.165 
Z-value...................: 2.224659 
P-value...................: 0.02610412 
Hosmer and Lemeshow r^2...: 0.041932 
Cox and Snell r^2.........: 0.056102 
Nagelkerke's pseudo r^2...: 0.075039 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.075436 
Standard error............: 0.038396 
Odds ratio (effect size)..: 0.927 
Lower 95% CI..............: 0.86 
Upper 95% CI..............: 1 
Z-value...................: -1.964674 
P-value...................: 0.04945202 
Hosmer and Lemeshow r^2...: 0.044274 
Cox and Snell r^2.........: 0.054755 
Nagelkerke's pseudo r^2...: 0.076081 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of CD163.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.075449 
Standard error............: 0.054386 
Odds ratio (effect size)..: 0.927 
Lower 95% CI..............: 0.834 
Upper 95% CI..............: 1.032 
Z-value...................: -1.38727 
P-value...................: 0.1653595 
Hosmer and Lemeshow r^2...: 0.09356 
Cox and Snell r^2.........: 0.121349 
Nagelkerke's pseudo r^2...: 0.161992 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.041333 
Standard error............: 0.064428 
Odds ratio (effect size)..: 0.96 
Lower 95% CI..............: 0.846 
Upper 95% CI..............: 1.089 
Z-value...................: -0.641535 
P-value...................: 0.5211748 
Hosmer and Lemeshow r^2...: 0.025287 
Cox and Snell r^2.........: 0.02504 
Nagelkerke's pseudo r^2...: 0.039547 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.409518 
Standard error............: 0.069701 
Odds ratio (effect size)..: 1.506 
Lower 95% CI..............: 1.314 
Upper 95% CI..............: 1.727 
Z-value...................: 5.875386 
P-value...................: 4.218595e-09 
Hosmer and Lemeshow r^2...: 0.103656 
Cox and Snell r^2.........: 0.110522 
Nagelkerke's pseudo r^2...: 0.163269 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: IPH 
Effect size...............: 0.083677 
Standard error............: 0.054139 
Odds ratio (effect size)..: 1.087 
Lower 95% CI..............: 0.978 
Upper 95% CI..............: 1.209 
Z-value...................: 1.545596 
P-value...................: 0.1222021 
Hosmer and Lemeshow r^2...: 0.04143 
Cox and Snell r^2.........: 0.054192 
Nagelkerke's pseudo r^2...: 0.07329 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.158284 
Standard error............: 0.054249 
Odds ratio (effect size)..: 1.171 
Lower 95% CI..............: 1.053 
Upper 95% CI..............: 1.303 
Z-value...................: 2.917748 
P-value...................: 0.003525691 
Hosmer and Lemeshow r^2...: 0.045253 
Cox and Snell r^2.........: 0.060408 
Nagelkerke's pseudo r^2...: 0.080798 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.085355 
Standard error............: 0.055406 
Odds ratio (effect size)..: 0.918 
Lower 95% CI..............: 0.824 
Upper 95% CI..............: 1.024 
Z-value...................: -1.540537 
P-value...................: 0.1234294 
Hosmer and Lemeshow r^2...: 0.042802 
Cox and Snell r^2.........: 0.052984 
Nagelkerke's pseudo r^2...: 0.07362 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of PTGDS.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.002294 
Standard error............: 0.047625 
Odds ratio (effect size)..: 0.998 
Lower 95% CI..............: 0.909 
Upper 95% CI..............: 1.095 
Z-value...................: -0.048177 
P-value...................: 0.961575 
Hosmer and Lemeshow r^2...: 0.09189 
Cox and Snell r^2.........: 0.119317 
Nagelkerke's pseudo r^2...: 0.15928 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.042889 
Standard error............: 0.057906 
Odds ratio (effect size)..: 1.044 
Lower 95% CI..............: 0.932 
Upper 95% CI..............: 1.169 
Z-value...................: 0.740662 
P-value...................: 0.4588983 
Hosmer and Lemeshow r^2...: 0.025465 
Cox and Snell r^2.........: 0.025214 
Nagelkerke's pseudo r^2...: 0.039822 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.261483 
Standard error............: 0.060877 
Odds ratio (effect size)..: 1.299 
Lower 95% CI..............: 1.153 
Upper 95% CI..............: 1.463 
Z-value...................: 4.295294 
P-value...................: 1.744619e-05 
Hosmer and Lemeshow r^2...: 0.083753 
Cox and Snell r^2.........: 0.090293 
Nagelkerke's pseudo r^2...: 0.133385 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: IPH 
Effect size...............: 0.073021 
Standard error............: 0.048172 
Odds ratio (effect size)..: 1.076 
Lower 95% CI..............: 0.979 
Upper 95% CI..............: 1.182 
Z-value...................: 1.515854 
P-value...................: 0.1295563 
Hosmer and Lemeshow r^2...: 0.041354 
Cox and Snell r^2.........: 0.054096 
Nagelkerke's pseudo r^2...: 0.073161 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.149813 
Standard error............: 0.048134 
Odds ratio (effect size)..: 1.162 
Lower 95% CI..............: 1.057 
Upper 95% CI..............: 1.277 
Z-value...................: 3.112387 
P-value...................: 0.001855809 
Hosmer and Lemeshow r^2...: 0.046419 
Cox and Snell r^2.........: 0.061916 
Nagelkerke's pseudo r^2...: 0.082814 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.060746 
Standard error............: 0.048517 
Odds ratio (effect size)..: 0.941 
Lower 95% CI..............: 0.856 
Upper 95% CI..............: 1.035 
Z-value...................: -1.252045 
P-value...................: 0.2105535 
Hosmer and Lemeshow r^2...: 0.042013 
Cox and Snell r^2.........: 0.052033 
Nagelkerke's pseudo r^2...: 0.072299 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of LGALS9.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.086233 
Standard error............: 0.043684 
Odds ratio (effect size)..: 0.917 
Lower 95% CI..............: 0.842 
Upper 95% CI..............: 0.999 
Z-value...................: -1.97404 
P-value...................: 0.04837719 
Hosmer and Lemeshow r^2...: 0.095272 
Cox and Snell r^2.........: 0.123426 
Nagelkerke's pseudo r^2...: 0.164764 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.010381 
Standard error............: 0.05264 
Odds ratio (effect size)..: 1.01 
Lower 95% CI..............: 0.911 
Upper 95% CI..............: 1.12 
Z-value...................: 0.197216 
P-value...................: 0.8436588 
Hosmer and Lemeshow r^2...: 0.024831 
Cox and Snell r^2.........: 0.024595 
Nagelkerke's pseudo r^2...: 0.038844 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.28323 
Standard error............: 0.054023 
Odds ratio (effect size)..: 1.327 
Lower 95% CI..............: 1.194 
Upper 95% CI..............: 1.476 
Z-value...................: 5.242805 
P-value...................: 1.581537e-07 
Hosmer and Lemeshow r^2...: 0.093861 
Cox and Snell r^2.........: 0.100624 
Nagelkerke's pseudo r^2...: 0.148646 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: IPH 
Effect size...............: 0.038225 
Standard error............: 0.043573 
Odds ratio (effect size)..: 1.039 
Lower 95% CI..............: 0.954 
Upper 95% CI..............: 1.132 
Z-value...................: 0.877262 
P-value...................: 0.3803443 
Hosmer and Lemeshow r^2...: 0.039934 
Cox and Snell r^2.........: 0.052288 
Nagelkerke's pseudo r^2...: 0.070715 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.133967 
Standard error............: 0.043441 
Odds ratio (effect size)..: 1.143 
Lower 95% CI..............: 1.05 
Upper 95% CI..............: 1.245 
Z-value...................: 3.083904 
P-value...................: 0.002043035 
Hosmer and Lemeshow r^2...: 0.04615 
Cox and Snell r^2.........: 0.061568 
Nagelkerke's pseudo r^2...: 0.082349 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.066193 
Standard error............: 0.04489 
Odds ratio (effect size)..: 0.936 
Lower 95% CI..............: 0.857 
Upper 95% CI..............: 1.022 
Z-value...................: -1.474583 
P-value...................: 0.1403247 
Hosmer and Lemeshow r^2...: 0.04261 
Cox and Snell r^2.........: 0.052752 
Nagelkerke's pseudo r^2...: 0.073298 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of ACKR1.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.052575 
Standard error............: 0.051901 
Odds ratio (effect size)..: 0.949 
Lower 95% CI..............: 0.857 
Upper 95% CI..............: 1.05 
Z-value...................: -1.012996 
P-value...................: 0.311062 
Hosmer and Lemeshow r^2...: 0.092776 
Cox and Snell r^2.........: 0.120395 
Nagelkerke's pseudo r^2...: 0.160719 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.060932 
Standard error............: 0.064186 
Odds ratio (effect size)..: 1.063 
Lower 95% CI..............: 0.937 
Upper 95% CI..............: 1.205 
Z-value...................: 0.949301 
P-value...................: 0.3424676 
Hosmer and Lemeshow r^2...: 0.02591 
Cox and Snell r^2.........: 0.025649 
Nagelkerke's pseudo r^2...: 0.040509 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.244417 
Standard error............: 0.065435 
Odds ratio (effect size)..: 1.277 
Lower 95% CI..............: 1.123 
Upper 95% CI..............: 1.452 
Z-value...................: 3.735253 
P-value...................: 0.0001875269 
Hosmer and Lemeshow r^2...: 0.078344 
Cox and Snell r^2.........: 0.084716 
Nagelkerke's pseudo r^2...: 0.125146 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: IPH 
Effect size...............: 0.125615 
Standard error............: 0.053553 
Odds ratio (effect size)..: 1.134 
Lower 95% CI..............: 1.021 
Upper 95% CI..............: 1.259 
Z-value...................: 2.345623 
P-value...................: 0.01899529 
Hosmer and Lemeshow r^2...: 0.04438 
Cox and Snell r^2.........: 0.057937 
Nagelkerke's pseudo r^2...: 0.078355 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.143234 
Standard error............: 0.052693 
Odds ratio (effect size)..: 1.154 
Lower 95% CI..............: 1.041 
Upper 95% CI..............: 1.28 
Z-value...................: 2.718251 
P-value...................: 0.006562805 
Hosmer and Lemeshow r^2...: 0.044266 
Cox and Snell r^2.........: 0.05913 
Nagelkerke's pseudo r^2...: 0.079089 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.089689 
Standard error............: 0.053336 
Odds ratio (effect size)..: 0.914 
Lower 95% CI..............: 0.823 
Upper 95% CI..............: 1.015 
Z-value...................: -1.68158 
P-value...................: 0.09265026 
Hosmer and Lemeshow r^2...: 0.043231 
Cox and Snell r^2.........: 0.0535 
Nagelkerke's pseudo r^2...: 0.074337 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of NT5DC2.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.088467 
Standard error............: 0.047866 
Odds ratio (effect size)..: 0.915 
Lower 95% CI..............: 0.833 
Upper 95% CI..............: 1.005 
Z-value...................: -1.848197 
P-value...................: 0.06457391 
Hosmer and Lemeshow r^2...: 0.09485 
Cox and Snell r^2.........: 0.122914 
Nagelkerke's pseudo r^2...: 0.164081 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.009498 
Standard error............: 0.057694 
Odds ratio (effect size)..: 0.991 
Lower 95% CI..............: 0.885 
Upper 95% CI..............: 1.109 
Z-value...................: -0.164626 
P-value...................: 0.8692382 
Hosmer and Lemeshow r^2...: 0.024817 
Cox and Snell r^2.........: 0.02458 
Nagelkerke's pseudo r^2...: 0.038821 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.252813 
Standard error............: 0.056456 
Odds ratio (effect size)..: 1.288 
Lower 95% CI..............: 1.153 
Upper 95% CI..............: 1.438 
Z-value...................: 4.478086 
P-value...................: 7.531535e-06 
Hosmer and Lemeshow r^2...: 0.084519 
Cox and Snell r^2.........: 0.09108 
Nagelkerke's pseudo r^2...: 0.134548 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: IPH 
Effect size...............: 0.067459 
Standard error............: 0.047908 
Odds ratio (effect size)..: 1.07 
Lower 95% CI..............: 0.974 
Upper 95% CI..............: 1.175 
Z-value...................: 1.408096 
P-value...................: 0.1591027 
Hosmer and Lemeshow r^2...: 0.04105 
Cox and Snell r^2.........: 0.053708 
Nagelkerke's pseudo r^2...: 0.072636 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.087253 
Standard error............: 0.047485 
Odds ratio (effect size)..: 1.091 
Lower 95% CI..............: 0.994 
Upper 95% CI..............: 1.198 
Z-value...................: 1.837496 
P-value...................: 0.06613669 
Hosmer and Lemeshow r^2...: 0.040503 
Cox and Snell r^2.........: 0.054243 
Nagelkerke's pseudo r^2...: 0.072552 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.062492 
Standard error............: 0.049513 
Odds ratio (effect size)..: 0.939 
Lower 95% CI..............: 0.853 
Upper 95% CI..............: 1.035 
Z-value...................: -1.262152 
P-value...................: 0.2068942 
Hosmer and Lemeshow r^2...: 0.042045 
Cox and Snell r^2.........: 0.052071 
Nagelkerke's pseudo r^2...: 0.072352 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of TGFBI.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.057678 
Standard error............: 0.043112 
Odds ratio (effect size)..: 0.944 
Lower 95% CI..............: 0.867 
Upper 95% CI..............: 1.027 
Z-value...................: -1.337876 
P-value...................: 0.1809368 
Hosmer and Lemeshow r^2...: 0.093438 
Cox and Snell r^2.........: 0.1212 
Nagelkerke's pseudo r^2...: 0.161794 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.002257 
Standard error............: 0.052135 
Odds ratio (effect size)..: 1.002 
Lower 95% CI..............: 0.905 
Upper 95% CI..............: 1.11 
Z-value...................: 0.043284 
P-value...................: 0.965475 
Hosmer and Lemeshow r^2...: 0.024786 
Cox and Snell r^2.........: 0.02455 
Nagelkerke's pseudo r^2...: 0.038774 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.246708 
Standard error............: 0.051638 
Odds ratio (effect size)..: 1.28 
Lower 95% CI..............: 1.157 
Upper 95% CI..............: 1.416 
Z-value...................: 4.777668 
P-value...................: 1.773395e-06 
Hosmer and Lemeshow r^2...: 0.087941 
Cox and Snell r^2.........: 0.094587 
Nagelkerke's pseudo r^2...: 0.139728 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: IPH 
Effect size...............: 0.060259 
Standard error............: 0.043206 
Odds ratio (effect size)..: 1.062 
Lower 95% CI..............: 0.976 
Upper 95% CI..............: 1.156 
Z-value...................: 1.394694 
P-value...................: 0.1631081 
Hosmer and Lemeshow r^2...: 0.041017 
Cox and Snell r^2.........: 0.053666 
Nagelkerke's pseudo r^2...: 0.072579 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.093817 
Standard error............: 0.042761 
Odds ratio (effect size)..: 1.098 
Lower 95% CI..............: 1.01 
Upper 95% CI..............: 1.194 
Z-value...................: 2.193985 
P-value...................: 0.02823646 
Hosmer and Lemeshow r^2...: 0.041815 
Cox and Snell r^2.........: 0.05595 
Nagelkerke's pseudo r^2...: 0.074835 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.053082 
Standard error............: 0.044558 
Odds ratio (effect size)..: 0.948 
Lower 95% CI..............: 0.869 
Upper 95% CI..............: 1.035 
Z-value...................: -1.191303 
P-value...................: 0.2335346 
Hosmer and Lemeshow r^2...: 0.041874 
Cox and Snell r^2.........: 0.051865 
Nagelkerke's pseudo r^2...: 0.072065 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of C1QC.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.064073 
Standard error............: 0.040263 
Odds ratio (effect size)..: 0.938 
Lower 95% CI..............: 0.867 
Upper 95% CI..............: 1.015 
Z-value...................: -1.59136 
P-value...................: 0.1115285 
Hosmer and Lemeshow r^2...: 0.094081 
Cox and Snell r^2.........: 0.121981 
Nagelkerke's pseudo r^2...: 0.162836 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.028914 
Standard error............: 0.048432 
Odds ratio (effect size)..: 1.029 
Lower 95% CI..............: 0.936 
Upper 95% CI..............: 1.132 
Z-value...................: 0.597005 
P-value...................: 0.5505041 
Hosmer and Lemeshow r^2...: 0.025222 
Cox and Snell r^2.........: 0.024977 
Nagelkerke's pseudo r^2...: 0.039448 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.270806 
Standard error............: 0.049264 
Odds ratio (effect size)..: 1.311 
Lower 95% CI..............: 1.19 
Upper 95% CI..............: 1.444 
Z-value...................: 5.497042 
P-value...................: 3.862141e-08 
Hosmer and Lemeshow r^2...: 0.096679 
Cox and Snell r^2.........: 0.103482 
Nagelkerke's pseudo r^2...: 0.152869 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: IPH 
Effect size...............: 0.041439 
Standard error............: 0.040232 
Odds ratio (effect size)..: 1.042 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.128 
Z-value...................: 1.029986 
P-value...................: 0.3030165 
Hosmer and Lemeshow r^2...: 0.040202 
Cox and Snell r^2.........: 0.052628 
Nagelkerke's pseudo r^2...: 0.071176 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.112417 
Standard error............: 0.040069 
Odds ratio (effect size)..: 1.119 
Lower 95% CI..............: 1.034 
Upper 95% CI..............: 1.21 
Z-value...................: 2.80556 
P-value...................: 0.005022928 
Hosmer and Lemeshow r^2...: 0.044627 
Cox and Snell r^2.........: 0.059597 
Nagelkerke's pseudo r^2...: 0.079713 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.055125 
Standard error............: 0.041522 
Odds ratio (effect size)..: 0.946 
Lower 95% CI..............: 0.872 
Upper 95% CI..............: 1.027 
Z-value...................: -1.327603 
P-value...................: 0.1843093 
Hosmer and Lemeshow r^2...: 0.04221 
Cox and Snell r^2.........: 0.052271 
Nagelkerke's pseudo r^2...: 0.072629 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

Analysis of S100A9.

- processing CalcificationPlaque

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.071335 
Standard error............: 0.052842 
Odds ratio (effect size)..: 0.931 
Lower 95% CI..............: 0.84 
Upper 95% CI..............: 1.033 
Z-value...................: -1.349985 
P-value...................: 0.1770207 
Hosmer and Lemeshow r^2...: 0.093466 
Cox and Snell r^2.........: 0.121235 
Nagelkerke's pseudo r^2...: 0.16184 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing CollagenPlaque

filter: removed 280 rows (26%), 812 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.059073 
Standard error............: 0.063353 
Odds ratio (effect size)..: 0.943 
Lower 95% CI..............: 0.833 
Upper 95% CI..............: 1.067 
Z-value...................: -0.932443 
P-value...................: 0.3511076 
Hosmer and Lemeshow r^2...: 0.025844 
Cox and Snell r^2.........: 0.025585 
Nagelkerke's pseudo r^2...: 0.040408 
Sample size of AE DB......: 1092 
Sample size of model......: 812 
Missing data %............: 25.64103 

- processing Fat10Perc

filter: removed 256 rows (23%), 836 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.36006 
Standard error............: 0.06658 
Odds ratio (effect size)..: 1.433 
Lower 95% CI..............: 1.258 
Upper 95% CI..............: 1.633 
Z-value...................: 5.407928 
P-value...................: 6.375787e-08 
Hosmer and Lemeshow r^2...: 0.096491 
Cox and Snell r^2.........: 0.103292 
Nagelkerke's pseudo r^2...: 0.152588 
Sample size of AE DB......: 1092 
Sample size of model......: 836 
Missing data %............: 23.44322 

- processing IPH

filter: removed 279 rows (26%), 813 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: IPH 
Effect size...............: 0.055833 
Standard error............: 0.053006 
Odds ratio (effect size)..: 1.057 
Lower 95% CI..............: 0.953 
Upper 95% CI..............: 1.173 
Z-value...................: 1.053329 
P-value...................: 0.29219 
Hosmer and Lemeshow r^2...: 0.040248 
Cox and Snell r^2.........: 0.052687 
Nagelkerke's pseudo r^2...: 0.071256 
Sample size of AE DB......: 1092 
Sample size of model......: 813 
Missing data %............: 25.54945 

- processing MAC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: MAC_binned 
Effect size...............: 0.0745 
Standard error............: 0.052318 
Odds ratio (effect size)..: 1.077 
Lower 95% CI..............: 0.972 
Upper 95% CI..............: 1.194 
Z-value...................: 1.423982 
P-value...................: 0.1544516 
Hosmer and Lemeshow r^2...: 0.039279 
Cox and Snell r^2.........: 0.052648 
Nagelkerke's pseudo r^2...: 0.070418 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048 

- processing SMC_binned

filter: removed 286 rows (26%), 806 rows remainingfilter_if: no rows removed

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: SMC_binned 
Effect size...............: -0.143705 
Standard error............: 0.05431 
Odds ratio (effect size)..: 0.866 
Lower 95% CI..............: 0.779 
Upper 95% CI..............: 0.963 
Z-value...................: -2.645998 
P-value...................: 0.008145018 
Hosmer and Lemeshow r^2...: 0.047324 
Cox and Snell r^2.........: 0.058415 
Nagelkerke's pseudo r^2...: 0.081167 
Sample size of AE DB......: 1092 
Sample size of model......: 806 
Missing data %............: 26.19048

cat("Edit the column names...\n")

Edit the column names...

colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")

Correct the variable types...

GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

Writing results to Excel-file...

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")

Removing intermediate files...

rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque CONVOCALS_downstream expression levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

stroke
TIA
retinal infarction
amaurosis fugax
asymptomatic

Model 1

In this model we correct for Age, Gender, and year of surgery.

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, family = binomial(link = "logit"))

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }


Analysis of SCGB3A2.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -3.037e+00  1.286e+00  -2.361 0.018235 *  
currentDF[, TARGET] -9.404e-02  8.181e-02  -1.149 0.250377    
Age                  3.812e-02  1.155e-02   3.300 0.000967 ***
Gendermale          -4.229e-01  2.448e-01  -1.728 0.084028 .  
ORdate_epoch         2.467e-04  7.871e-05   3.134 0.001725 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 691.59  on 1087  degrees of freedom
AIC: 701.59

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.094037 
Standard error............: 0.081812 
Odds ratio (effect size)..: 0.91 
Lower 95% CI..............: 0.775 
Upper 95% CI..............: 1.069 
Z-value...................: -1.149434 
P-value...................: 0.250377 
Hosmer and Lemeshow r^2...: 0.03659 
Cox and Snell r^2.........: 0.023767 
Nagelkerke's pseudo r^2...: 0.04933 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of LIX1.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
          0.3451027           -0.3238526            0.0361584           -0.4158310            0.0001646  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 684.4    AIC: 694.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)          3.451e-01  1.769e+00   0.195  0.84530   
currentDF[, TARGET] -3.239e-01  1.117e-01  -2.900  0.00373 **
Age                  3.616e-02  1.148e-02   3.149  0.00164 **
Gendermale          -4.158e-01  2.446e-01  -1.700  0.08914 . 
ORdate_epoch         1.646e-04  8.075e-05   2.039  0.04145 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 684.38  on 1087  degrees of freedom
AIC: 694.38

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.323853 
Standard error............: 0.111667 
Odds ratio (effect size)..: 0.723 
Lower 95% CI..............: 0.581 
Upper 95% CI..............: 0.9 
Z-value...................: -2.900151 
P-value...................: 0.00372983 
Hosmer and Lemeshow r^2...: 0.046643 
Cox and Snell r^2.........: 0.030197 
Nagelkerke's pseudo r^2...: 0.062676 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of IGSF9B.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -2.982e+00  1.285e+00  -2.320  0.02035 *  
currentDF[, TARGET] -1.161e-01  8.146e-02  -1.425  0.15418    
Age                  3.791e-02  1.152e-02   3.291  0.00100 ***
Gendermale          -4.263e-01  2.448e-01  -1.741  0.08161 .  
ORdate_epoch         2.566e-04  7.957e-05   3.225  0.00126 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 690.91  on 1087  degrees of freedom
AIC: 700.91

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.11607 
Standard error............: 0.081457 
Odds ratio (effect size)..: 0.89 
Lower 95% CI..............: 0.759 
Upper 95% CI..............: 1.045 
Z-value...................: -1.42492 
P-value...................: 0.1541803 
Hosmer and Lemeshow r^2...: 0.037546 
Cox and Snell r^2.........: 0.02438 
Nagelkerke's pseudo r^2...: 0.050603 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of ALB.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -1.4881524           -0.2721211            0.0375839           -0.4470213            0.0002238  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 685.7    AIC: 695.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -1.488e+00  1.436e+00  -1.036  0.30008   
currentDF[, TARGET] -2.721e-01  1.030e-01  -2.642  0.00823 **
Age                  3.758e-02  1.154e-02   3.258  0.00112 **
Gendermale          -4.470e-01  2.452e-01  -1.823  0.06827 . 
ORdate_epoch         2.238e-04  7.715e-05   2.901  0.00372 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 685.71  on 1087  degrees of freedom
AIC: 695.71

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.272121 
Standard error............: 0.102984 
Odds ratio (effect size)..: 0.762 
Lower 95% CI..............: 0.623 
Upper 95% CI..............: 0.932 
Z-value...................: -2.642366 
P-value...................: 0.008232908 
Hosmer and Lemeshow r^2...: 0.044778 
Cox and Snell r^2.........: 0.029007 
Nagelkerke's pseudo r^2...: 0.060207 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of OR10A4.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -1.5257886           -0.2693501            0.0374597           -0.4416734            0.0002163  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 684.8    AIC: 694.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -1.526e+00  1.417e+00  -1.077  0.28145   
currentDF[, TARGET] -2.694e-01  9.620e-02  -2.800  0.00511 **
Age                  3.746e-02  1.156e-02   3.241  0.00119 **
Gendermale          -4.417e-01  2.451e-01  -1.802  0.07160 . 
ORdate_epoch         2.163e-04  7.735e-05   2.796  0.00517 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 684.80  on 1087  degrees of freedom
AIC: 694.8

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.26935 
Standard error............: 0.096203 
Odds ratio (effect size)..: 0.764 
Lower 95% CI..............: 0.633 
Upper 95% CI..............: 0.922 
Z-value...................: -2.799811 
P-value...................: 0.005113252 
Hosmer and Lemeshow r^2...: 0.046046 
Cox and Snell r^2.........: 0.029816 
Nagelkerke's pseudo r^2...: 0.061886 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of RASEF.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -2.2118383           -0.1663868            0.0378798           -0.4331732            0.0002409  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 690.2    AIC: 700.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -2.212e+00  1.433e+00  -1.544  0.12269   
currentDF[, TARGET] -1.664e-01  1.029e-01  -1.616  0.10600   
Age                  3.788e-02  1.153e-02   3.285  0.00102 **
Gendermale          -4.332e-01  2.448e-01  -1.769  0.07687 . 
ORdate_epoch         2.409e-04  7.753e-05   3.108  0.00189 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 690.20  on 1087  degrees of freedom
AIC: 700.2

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.166387 
Standard error............: 0.102935 
Odds ratio (effect size)..: 0.847 
Lower 95% CI..............: 0.692 
Upper 95% CI..............: 1.036 
Z-value...................: -1.616429 
P-value...................: 0.1060016 
Hosmer and Lemeshow r^2...: 0.038529 
Cox and Snell r^2.........: 0.02501 
Nagelkerke's pseudo r^2...: 0.05191 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of NEDD4.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -1.7277078           -0.1830158            0.0375738           -0.4190430            0.0002337  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 689.9    AIC: 699.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -1.728e+00  1.559e+00  -1.108  0.26768   
currentDF[, TARGET] -1.830e-01  1.075e-01  -1.702  0.08868 . 
Age                  3.757e-02  1.152e-02   3.260  0.00111 **
Gendermale          -4.190e-01  2.441e-01  -1.717  0.08606 . 
ORdate_epoch         2.337e-04  7.701e-05   3.035  0.00241 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 689.91  on 1087  degrees of freedom
AIC: 699.91

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.183016 
Standard error............: 0.107504 
Odds ratio (effect size)..: 0.833 
Lower 95% CI..............: 0.675 
Upper 95% CI..............: 1.028 
Z-value...................: -1.702408 
P-value...................: 0.08867889 
Hosmer and Lemeshow r^2...: 0.038928 
Cox and Snell r^2.........: 0.025266 
Nagelkerke's pseudo r^2...: 0.052441 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of TCL1A.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
          0.7232562           -0.3339509            0.0363137           -0.4067467            0.0001575  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 685.3    AIC: 695.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)          0.7232562  1.9171015   0.377  0.70598   
currentDF[, TARGET] -0.3339509  0.1213191  -2.753  0.00591 **
Age                  0.0363137  0.0115045   3.156  0.00160 **
Gendermale          -0.4067467  0.2443076  -1.665  0.09593 . 
ORdate_epoch         0.0001575  0.0000818   1.926  0.05416 . 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 685.26  on 1087  degrees of freedom
AIC: 695.26

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.333951 
Standard error............: 0.121319 
Odds ratio (effect size)..: 0.716 
Lower 95% CI..............: 0.565 
Upper 95% CI..............: 0.908 
Z-value...................: -2.752665 
P-value...................: 0.005911234 
Hosmer and Lemeshow r^2...: 0.045404 
Cox and Snell r^2.........: 0.029407 
Nagelkerke's pseudo r^2...: 0.061036 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of FBXO15.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -1.2188788           -0.2202777            0.0374809           -0.4402907            0.0002216  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 688.6    AIC: 698.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -1.219e+00  1.630e+00  -0.748  0.45471   
currentDF[, TARGET] -2.203e-01  1.092e-01  -2.017  0.04367 * 
Age                  3.748e-02  1.153e-02   3.251  0.00115 **
Gendermale          -4.403e-01  2.449e-01  -1.798  0.07226 . 
ORdate_epoch         2.216e-04  7.715e-05   2.872  0.00408 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 688.65  on 1087  degrees of freedom
AIC: 698.65

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.220278 
Standard error............: 0.109197 
Odds ratio (effect size)..: 0.802 
Lower 95% CI..............: 0.648 
Upper 95% CI..............: 0.994 
Z-value...................: -2.017248 
P-value...................: 0.04366967 
Hosmer and Lemeshow r^2...: 0.040692 
Cox and Snell r^2.........: 0.026395 
Nagelkerke's pseudo r^2...: 0.054786 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of F5.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -1.7189782           -0.2786394            0.0383152           -0.4422906            0.0002416  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 686.3    AIC: 696.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -1.719e+00  1.415e+00  -1.215 0.224331    
currentDF[, TARGET] -2.786e-01  1.106e-01  -2.520 0.011728 *  
Age                  3.832e-02  1.156e-02   3.315 0.000918 ***
Gendermale          -4.423e-01  2.449e-01  -1.806 0.070931 .  
ORdate_epoch         2.416e-04  7.696e-05   3.140 0.001692 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 686.31  on 1087  degrees of freedom
AIC: 696.31

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.278639 
Standard error............: 0.110561 
Odds ratio (effect size)..: 0.757 
Lower 95% CI..............: 0.609 
Upper 95% CI..............: 0.94 
Z-value...................: -2.520235 
P-value...................: 0.01172764 
Hosmer and Lemeshow r^2...: 0.043948 
Cox and Snell r^2.........: 0.028477 
Nagelkerke's pseudo r^2...: 0.059106 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of TMEM212.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -0.9585654           -0.2829589            0.0378660           -0.4396764            0.0002281  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 686.5    AIC: 696.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -9.586e-01  1.578e+00  -0.607  0.54360   
currentDF[, TARGET] -2.830e-01  1.144e-01  -2.473  0.01342 * 
Age                  3.787e-02  1.154e-02   3.281  0.00103 **
Gendermale          -4.397e-01  2.449e-01  -1.795  0.07259 . 
ORdate_epoch         2.281e-04  7.708e-05   2.960  0.00308 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 686.48  on 1087  degrees of freedom
AIC: 696.48

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.282959 
Standard error............: 0.11444 
Odds ratio (effect size)..: 0.754 
Lower 95% CI..............: 0.602 
Upper 95% CI..............: 0.943 
Z-value...................: -2.472546 
P-value...................: 0.01341544 
Hosmer and Lemeshow r^2...: 0.043708 
Cox and Snell r^2.........: 0.028324 
Nagelkerke's pseudo r^2...: 0.058789 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of PTPRD.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -2.854e+00  1.477e+00  -1.932  0.05330 . 
currentDF[, TARGET] -9.630e-02  1.801e-01  -0.535  0.59289   
Age                  3.705e-02  1.150e-02   3.222  0.00127 **
Gendermale          -3.957e-01  2.435e-01  -1.625  0.10417   
ORdate_epoch         2.313e-04  7.726e-05   2.993  0.00276 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.60  on 1087  degrees of freedom
AIC: 702.6

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.096298 
Standard error............: 0.180111 
Odds ratio (effect size)..: 0.908 
Lower 95% CI..............: 0.638 
Upper 95% CI..............: 1.293 
Z-value...................: -0.534658 
P-value...................: 0.5928864 
Hosmer and Lemeshow r^2...: 0.035184 
Cox and Snell r^2.........: 0.022864 
Nagelkerke's pseudo r^2...: 0.047456 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of CYP46A1.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -0.8533859           -0.2717294            0.0378577           -0.4312450            0.0002163  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 686.6    AIC: 696.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -8.534e-01  1.625e+00  -0.525  0.59951   
currentDF[, TARGET] -2.717e-01  1.120e-01  -2.427  0.01523 * 
Age                  3.786e-02  1.154e-02   3.282  0.00103 **
Gendermale          -4.312e-01  2.446e-01  -1.763  0.07793 . 
ORdate_epoch         2.163e-04  7.755e-05   2.789  0.00528 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 686.61  on 1087  degrees of freedom
AIC: 696.61

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.271729 
Standard error............: 0.111967 
Odds ratio (effect size)..: 0.762 
Lower 95% CI..............: 0.612 
Upper 95% CI..............: 0.949 
Z-value...................: -2.426861 
P-value...................: 0.01523007 
Hosmer and Lemeshow r^2...: 0.043532 
Cox and Snell r^2.........: 0.028211 
Nagelkerke's pseudo r^2...: 0.058555 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of OR2T33.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -1.6388650           -0.2392789            0.0375441           -0.4305513            0.0002332  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 687.7    AIC: 697.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -1.639e+00  1.467e+00  -1.117  0.26388   
currentDF[, TARGET] -2.393e-01  1.071e-01  -2.235  0.02544 * 
Age                  3.754e-02  1.154e-02   3.252  0.00114 **
Gendermale          -4.306e-01  2.446e-01  -1.761  0.07832 . 
ORdate_epoch         2.332e-04  7.713e-05   3.023  0.00250 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 687.67  on 1087  degrees of freedom
AIC: 697.67

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.239279 
Standard error............: 0.107073 
Odds ratio (effect size)..: 0.787 
Lower 95% CI..............: 0.638 
Upper 95% CI..............: 0.971 
Z-value...................: -2.234723 
P-value...................: 0.02543551 
Hosmer and Lemeshow r^2...: 0.042057 
Cox and Snell r^2.........: 0.027269 
Nagelkerke's pseudo r^2...: 0.056599 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of SORBS2.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -2.2964961           -0.1630857            0.0376319           -0.4331131            0.0002485  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 690  AIC: 700

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -2.296e+00  1.384e+00  -1.659  0.09702 . 
currentDF[, TARGET] -1.631e-01  9.407e-02  -1.734  0.08299 . 
Age                  3.763e-02  1.156e-02   3.255  0.00114 **
Gendermale          -4.331e-01  2.447e-01  -1.770  0.07667 . 
ORdate_epoch         2.485e-04  7.755e-05   3.204  0.00136 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 689.98  on 1087  degrees of freedom
AIC: 699.98

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.163086 
Standard error............: 0.094074 
Odds ratio (effect size)..: 0.85 
Lower 95% CI..............: 0.706 
Upper 95% CI..............: 1.022 
Z-value...................: -1.733582 
P-value...................: 0.08299233 
Hosmer and Lemeshow r^2...: 0.038842 
Cox and Snell r^2.........: 0.025211 
Nagelkerke's pseudo r^2...: 0.052327 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of ITGA7.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -2.162e+00  1.547e+00  -1.398  0.16221   
currentDF[, TARGET] -1.511e-01  1.220e-01  -1.238  0.21572   
Age                  3.757e-02  1.152e-02   3.260  0.00111 **
Gendermale          -4.180e-01  2.442e-01  -1.712  0.08697 . 
ORdate_epoch         2.385e-04  7.711e-05   3.093  0.00198 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 691.34  on 1087  degrees of freedom
AIC: 701.34

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.151083 
Standard error............: 0.122039 
Odds ratio (effect size)..: 0.86 
Lower 95% CI..............: 0.677 
Upper 95% CI..............: 1.092 
Z-value...................: -1.237986 
P-value...................: 0.2157211 
Hosmer and Lemeshow r^2...: 0.036938 
Cox and Snell r^2.........: 0.02399 
Nagelkerke's pseudo r^2...: 0.049793 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of FOS.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.374e+00  1.288e+00  -2.620  0.00880 **
currentDF[, TARGET]  4.517e-02  5.971e-02   0.756  0.44939   
Age                  3.742e-02  1.148e-02   3.260  0.00111 **
Gendermale          -4.034e-01  2.437e-01  -1.655  0.09791 . 
ORdate_epoch         2.159e-04  7.935e-05   2.721  0.00650 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.31  on 1087  degrees of freedom
AIC: 702.31

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.045167 
Standard error............: 0.059712 
Odds ratio (effect size)..: 1.046 
Lower 95% CI..............: 0.931 
Upper 95% CI..............: 1.176 
Z-value...................: 0.756425 
P-value...................: 0.4493945 
Hosmer and Lemeshow r^2...: 0.035584 
Cox and Snell r^2.........: 0.023121 
Nagelkerke's pseudo r^2...: 0.047989 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of HMOX1.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)  currentDF[, TARGET]                  Age           Gendermale         ORdate_epoch  
         -4.6679839            0.2268790            0.0368180           -0.4321958            0.0002149  

Degrees of Freedom: 1091 Total (i.e. Null);  1087 Residual
Null Deviance:      717.9 
Residual Deviance: 687.6    AIC: 697.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -4.668e+00  1.433e+00  -3.257  0.00113 **
currentDF[, TARGET]  2.269e-01  1.030e-01   2.202  0.02769 * 
Age                  3.682e-02  1.148e-02   3.208  0.00133 **
Gendermale          -4.322e-01  2.443e-01  -1.769  0.07692 . 
ORdate_epoch         2.149e-04  7.771e-05   2.766  0.00567 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 687.62  on 1087  degrees of freedom
AIC: 697.62

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.226879 
Standard error............: 0.103049 
Odds ratio (effect size)..: 1.255 
Lower 95% CI..............: 1.025 
Upper 95% CI..............: 1.535 
Z-value...................: 2.201671 
P-value...................: 0.02768856 
Hosmer and Lemeshow r^2...: 0.042125 
Cox and Snell r^2.........: 0.027312 
Nagelkerke's pseudo r^2...: 0.056688 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of LAPTM5.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.418e+00  1.298e+00  -2.633  0.00845 **
currentDF[, TARGET]  3.047e-02  4.689e-02   0.650  0.51579   
Age                  3.749e-02  1.150e-02   3.261  0.00111 **
Gendermale          -3.982e-01  2.435e-01  -1.635  0.10199   
ORdate_epoch         2.235e-04  7.765e-05   2.879  0.00399 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.47  on 1087  degrees of freedom
AIC: 702.47

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.030471 
Standard error............: 0.04689 
Odds ratio (effect size)..: 1.031 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 1.13 
Z-value...................: 0.649851 
P-value...................: 0.5157884 
Hosmer and Lemeshow r^2...: 0.035371 
Cox and Snell r^2.........: 0.022984 
Nagelkerke's pseudo r^2...: 0.047704 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of MMP9.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.387e+00  1.287e+00  -2.631  0.00852 **
currentDF[, TARGET]  3.395e-02  5.040e-02   0.674  0.50054   
Age                  3.731e-02  1.150e-02   3.245  0.00117 **
Gendermale          -3.997e-01  2.436e-01  -1.641  0.10077   
ORdate_epoch         2.213e-04  7.782e-05   2.844  0.00445 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.43  on 1087  degrees of freedom
AIC: 702.43

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.033949 
Standard error............: 0.050396 
Odds ratio (effect size)..: 1.035 
Lower 95% CI..............: 0.937 
Upper 95% CI..............: 1.142 
Z-value...................: 0.673637 
P-value...................: 0.5005419 
Hosmer and Lemeshow r^2...: 0.035419 
Cox and Snell r^2.........: 0.023015 
Nagelkerke's pseudo r^2...: 0.047769 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of SIGLEC1.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.398e+00  1.289e+00  -2.636  0.00839 **
currentDF[, TARGET]  3.961e-02  5.573e-02   0.711  0.47725   
Age                  3.732e-02  1.149e-02   3.247  0.00117 **
Gendermale          -4.029e-01  2.436e-01  -1.654  0.09820 . 
ORdate_epoch         2.224e-04  7.773e-05   2.861  0.00422 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.38  on 1087  degrees of freedom
AIC: 702.38

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.039607 
Standard error............: 0.055727 
Odds ratio (effect size)..: 1.04 
Lower 95% CI..............: 0.933 
Upper 95% CI..............: 1.16 
Z-value...................: 0.71074 
P-value...................: 0.4772452 
Hosmer and Lemeshow r^2...: 0.035489 
Cox and Snell r^2.........: 0.02306 
Nagelkerke's pseudo r^2...: 0.047862 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of FTL.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.668e+00  1.325e+00  -2.767  0.00565 **
currentDF[, TARGET]  5.236e-02  4.653e-02   1.125  0.26054   
Age                  3.752e-02  1.150e-02   3.264  0.00110 **
Gendermale          -4.029e-01  2.438e-01  -1.653  0.09837 . 
ORdate_epoch         2.207e-04  7.776e-05   2.838  0.00453 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 691.64  on 1087  degrees of freedom
AIC: 701.64

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.052357 
Standard error............: 0.046535 
Odds ratio (effect size)..: 1.054 
Lower 95% CI..............: 0.962 
Upper 95% CI..............: 1.154 
Z-value...................: 1.125114 
P-value...................: 0.2605407 
Hosmer and Lemeshow r^2...: 0.036529 
Cox and Snell r^2.........: 0.023727 
Nagelkerke's pseudo r^2...: 0.049248 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of CD14.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.383e+00  1.293e+00  -2.616  0.00889 **
currentDF[, TARGET]  2.725e-02  4.785e-02   0.570  0.56895   
Age                  3.752e-02  1.150e-02   3.261  0.00111 **
Gendermale          -3.990e-01  2.435e-01  -1.638  0.10135   
ORdate_epoch         2.224e-04  7.794e-05   2.853  0.00433 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.57  on 1087  degrees of freedom
AIC: 702.57

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.027253 
Standard error............: 0.047846 
Odds ratio (effect size)..: 1.028 
Lower 95% CI..............: 0.936 
Upper 95% CI..............: 1.129 
Z-value...................: 0.569593 
P-value...................: 0.5689536 
Hosmer and Lemeshow r^2...: 0.035234 
Cox and Snell r^2.........: 0.022896 
Nagelkerke's pseudo r^2...: 0.047522 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of HCST.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.427e+00  1.306e+00  -2.624  0.00868 **
currentDF[, TARGET]  2.862e-02  4.870e-02   0.588  0.55680   
Age                  3.742e-02  1.150e-02   3.255  0.00113 **
Gendermale          -3.998e-01  2.436e-01  -1.642  0.10066   
ORdate_epoch         2.249e-04  7.746e-05   2.904  0.00369 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.54  on 1087  degrees of freedom
AIC: 702.54

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.028617 
Standard error............: 0.048701 
Odds ratio (effect size)..: 1.029 
Lower 95% CI..............: 0.935 
Upper 95% CI..............: 1.132 
Z-value...................: 0.587606 
P-value...................: 0.5567967 
Hosmer and Lemeshow r^2...: 0.035263 
Cox and Snell r^2.........: 0.022914 
Nagelkerke's pseudo r^2...: 0.04756 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of TIMP3.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.282e+00  1.275e+00  -2.574   0.0101 * 
currentDF[, TARGET]  2.683e-02  5.525e-02   0.486   0.6272   
Age                  3.719e-02  1.148e-02   3.240   0.0012 **
Gendermale          -3.988e-01  2.435e-01  -1.637   0.1015   
ORdate_epoch         2.178e-04  8.003e-05   2.721   0.0065 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.65  on 1087  degrees of freedom
AIC: 702.65

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.026833 
Standard error............: 0.05525 
Odds ratio (effect size)..: 1.027 
Lower 95% CI..............: 0.922 
Upper 95% CI..............: 1.145 
Z-value...................: 0.485653 
P-value...................: 0.6272132 
Hosmer and Lemeshow r^2...: 0.035111 
Cox and Snell r^2.........: 0.022817 
Nagelkerke's pseudo r^2...: 0.047359 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of CCL2.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -3.594e+00  1.304e+00  -2.755 0.005864 ** 
currentDF[, TARGET]  7.767e-02  5.890e-02   1.319 0.187283    
Age                  3.807e-02  1.149e-02   3.312 0.000926 ***
Gendermale          -3.908e-01  2.437e-01  -1.603 0.108866    
ORdate_epoch         2.129e-04  7.863e-05   2.707 0.006780 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 691.13  on 1087  degrees of freedom
AIC: 701.13

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.077668 
Standard error............: 0.058899 
Odds ratio (effect size)..: 1.081 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.213 
Z-value...................: 1.318659 
P-value...................: 0.1872832 
Hosmer and Lemeshow r^2...: 0.037228 
Cox and Snell r^2.........: 0.024176 
Nagelkerke's pseudo r^2...: 0.050178 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of SAT1.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.513e+00  1.301e+00  -2.700  0.00694 **
currentDF[, TARGET]  4.873e-02  4.956e-02   0.983  0.32541   
Age                  3.757e-02  1.149e-02   3.270  0.00108 **
Gendermale          -4.020e-01  2.436e-01  -1.650  0.09891 . 
ORdate_epoch         2.183e-04  7.814e-05   2.793  0.00522 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 691.92  on 1087  degrees of freedom
AIC: 701.92

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.048734 
Standard error............: 0.049557 
Odds ratio (effect size)..: 1.05 
Lower 95% CI..............: 0.953 
Upper 95% CI..............: 1.157 
Z-value...................: 0.983402 
P-value...................: 0.3254098 
Hosmer and Lemeshow r^2...: 0.036132 
Cox and Snell r^2.........: 0.023472 
Nagelkerke's pseudo r^2...: 0.048719 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of CD163.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.443e+00  1.301e+00  -2.646  0.00816 **
currentDF[, TARGET]  5.623e-02  7.459e-02   0.754  0.45098   
Age                  3.735e-02  1.148e-02   3.252  0.00115 **
Gendermale          -3.996e-01  2.435e-01  -1.641  0.10081   
ORdate_epoch         2.169e-04  7.882e-05   2.751  0.00594 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.32  on 1087  degrees of freedom
AIC: 702.32

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.056229 
Standard error............: 0.074595 
Odds ratio (effect size)..: 1.058 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.224 
Z-value...................: 0.753787 
P-value...................: 0.4509774 
Hosmer and Lemeshow r^2...: 0.035582 
Cox and Snell r^2.........: 0.023119 
Nagelkerke's pseudo r^2...: 0.047986 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of PTGDS.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.185e+00  1.284e+00  -2.481  0.01311 * 
currentDF[, TARGET] -2.587e-02  6.275e-02  -0.412  0.68021   
Age                  3.714e-02  1.150e-02   3.231  0.00123 **
Gendermale          -3.940e-01  2.436e-01  -1.617  0.10580   
ORdate_epoch         2.320e-04  7.761e-05   2.989  0.00280 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.72  on 1087  degrees of freedom
AIC: 702.72

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.025866 
Standard error............: 0.062754 
Odds ratio (effect size)..: 0.974 
Lower 95% CI..............: 0.862 
Upper 95% CI..............: 1.102 
Z-value...................: -0.41217 
P-value...................: 0.6802148 
Hosmer and Lemeshow r^2...: 0.035016 
Cox and Snell r^2.........: 0.022756 
Nagelkerke's pseudo r^2...: 0.047232 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of LGALS9.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.418e+00  1.309e+00  -2.611  0.00902 **
currentDF[, TARGET]  3.099e-02  5.865e-02   0.528  0.59722   
Age                  3.738e-02  1.149e-02   3.252  0.00114 **
Gendermale          -3.993e-01  2.435e-01  -1.639  0.10111   
ORdate_epoch         2.246e-04  7.752e-05   2.897  0.00377 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.61  on 1087  degrees of freedom
AIC: 702.61

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.030993 
Standard error............: 0.058654 
Odds ratio (effect size)..: 1.031 
Lower 95% CI..............: 0.919 
Upper 95% CI..............: 1.157 
Z-value...................: 0.528402 
P-value...................: 0.5972203 
Hosmer and Lemeshow r^2...: 0.035172 
Cox and Snell r^2.........: 0.022856 
Nagelkerke's pseudo r^2...: 0.04744 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of ACKR1.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.406e+00  1.290e+00  -2.640  0.00828 **
currentDF[, TARGET]  4.861e-02  6.993e-02   0.695  0.48701   
Age                  3.734e-02  1.149e-02   3.249  0.00116 **
Gendermale          -4.071e-01  2.439e-01  -1.669  0.09516 . 
ORdate_epoch         2.226e-04  7.763e-05   2.868  0.00413 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.40  on 1087  degrees of freedom
AIC: 702.4

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.048609 
Standard error............: 0.069932 
Odds ratio (effect size)..: 1.05 
Lower 95% CI..............: 0.915 
Upper 95% CI..............: 1.204 
Z-value...................: 0.69508 
P-value...................: 0.4870054 
Hosmer and Lemeshow r^2...: 0.035466 
Cox and Snell r^2.........: 0.023045 
Nagelkerke's pseudo r^2...: 0.047832 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of NT5DC2.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.385e+00  1.284e+00  -2.636  0.00839 **
currentDF[, TARGET]  5.763e-02  6.430e-02   0.896  0.37014   
Age                  3.738e-02  1.148e-02   3.257  0.00113 **
Gendermale          -3.952e-01  2.436e-01  -1.623  0.10466   
ORdate_epoch         2.138e-04  7.918e-05   2.700  0.00693 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.08  on 1087  degrees of freedom
AIC: 702.08

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.057629 
Standard error............: 0.064302 
Odds ratio (effect size)..: 1.059 
Lower 95% CI..............: 0.934 
Upper 95% CI..............: 1.202 
Z-value...................: 0.896215 
P-value...................: 0.3701377 
Hosmer and Lemeshow r^2...: 0.035904 
Cox and Snell r^2.........: 0.023326 
Nagelkerke's pseudo r^2...: 0.048416 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of TGFBI.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.364e+00  1.281e+00  -2.626  0.00863 **
currentDF[, TARGET]  5.234e-02  5.856e-02   0.894  0.37147   
Age                  3.748e-02  1.148e-02   3.265  0.00109 **
Gendermale          -3.913e-01  2.437e-01  -1.606  0.10833   
ORdate_epoch         2.104e-04  7.996e-05   2.631  0.00852 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.09  on 1087  degrees of freedom
AIC: 702.09

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.052337 
Standard error............: 0.05856 
Odds ratio (effect size)..: 1.054 
Lower 95% CI..............: 0.939 
Upper 95% CI..............: 1.182 
Z-value...................: 0.893729 
P-value...................: 0.371467 
Hosmer and Lemeshow r^2...: 0.035901 
Cox and Snell r^2.........: 0.023324 
Nagelkerke's pseudo r^2...: 0.048411 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of C1QC.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.520e+00  1.313e+00  -2.681  0.00733 **
currentDF[, TARGET]  4.526e-02  5.395e-02   0.839  0.40154   
Age                  3.746e-02  1.149e-02   3.259  0.00112 **
Gendermale          -4.032e-01  2.437e-01  -1.655  0.09796 . 
ORdate_epoch         2.245e-04  7.758e-05   2.894  0.00380 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.18  on 1087  degrees of freedom
AIC: 702.18

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.04526 
Standard error............: 0.053953 
Odds ratio (effect size)..: 1.046 
Lower 95% CI..............: 0.941 
Upper 95% CI..............: 1.163 
Z-value...................: 0.838876 
P-value...................: 0.4015387 
Hosmer and Lemeshow r^2...: 0.035766 
Cox and Snell r^2.........: 0.023238 
Nagelkerke's pseudo r^2...: 0.048231 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0 

Analysis of S100A9.

- processing AsymptSympt

filter: no rows removedfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    ORdate_epoch, family = binomial(link = "logit"), data = currentDF)

Coefficients:
 (Intercept)           Age    Gendermale  ORdate_epoch  
  -3.2562637     0.0372001    -0.3971546     0.0002279  

Degrees of Freedom: 1091 Total (i.e. Null);  1088 Residual
Null Deviance:      717.9 
Residual Deviance: 692.9    AIC: 700.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -3.280e+00  1.281e+00  -2.560  0.01048 * 
currentDF[, TARGET]  1.129e-02  7.006e-02   0.161  0.87201   
Age                  3.722e-02  1.149e-02   3.239  0.00120 **
Gendermale          -3.980e-01  2.435e-01  -1.634  0.10219   
ORdate_epoch         2.259e-04  7.816e-05   2.890  0.00385 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.86  on 1091  degrees of freedom
Residual deviance: 692.86  on 1087  degrees of freedom
AIC: 702.86

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.011286 
Standard error............: 0.070057 
Odds ratio (effect size)..: 1.011 
Lower 95% CI..............: 0.882 
Upper 95% CI..............: 1.16 
Z-value...................: 0.161104 
P-value...................: 0.8720113 
Hosmer and Lemeshow r^2...: 0.034817 
Cox and Snell r^2.........: 0.022628 
Nagelkerke's pseudo r^2...: 0.046967 
Sample size of AE DB......: 1092 
Sample size of model......: 1092 
Missing data %............: 0

cat("Edit the column names...\n")

Edit the column names...

colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")

Correct the variable types...

GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

Writing results to Excel-file...

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")

Removing intermediate files...

rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #              Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #              MedHx_CVD + stenose, 
    #            data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }


Analysis of SCGB3A2.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.450e+01  1.956e+03   0.007  0.99409   
currentDF[, TARGET]       -3.560e-02  9.153e-02  -0.389  0.69734   
Age                        4.426e-02  1.510e-02   2.930  0.00339 **
Gendermale                -3.616e-01  2.795e-01  -1.294  0.19573   
ORdate_epoch               2.271e-04  9.085e-05   2.500  0.01244 * 
Hypertension.compositeyes -4.686e-01  4.191e-01  -1.118  0.26356   
DiabetesStatusDiabetes    -1.799e-01  2.628e-01  -0.685  0.49358   
SmokerStatusEx-smoker     -5.611e-01  2.765e-01  -2.030  0.04240 * 
SmokerStatusNever smoked  -8.971e-01  3.502e-01  -2.562  0.01041 * 
Med.Statin.LLDyes         -1.260e-02  2.964e-01  -0.043  0.96609   
Med.all.antiplateletyes   -7.607e-01  4.875e-01  -1.561  0.11863   
GFR_MDRD                   5.495e-03  6.093e-03   0.902  0.36718   
BMI                       -5.531e-02  2.900e-02  -1.907  0.05648 . 
MedHx_CVDyes              -1.064e-01  2.361e-01  -0.451  0.65223   
stenose50-70%             -1.349e+01  1.956e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.956e+03  -0.008  0.99375   
stenose90-99%             -1.533e+01  1.956e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.578e+01  1.956e+03  -0.008  0.99357   
stenose50-99%             -6.844e-01  2.776e+03   0.000  0.99980   
stenose70-99%             -5.054e-01  2.129e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.18  on 936  degrees of freedom
AIC: 602.18

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SCGB3A2 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SCGB3A2 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.035598 
Standard error............: 0.09153 
Odds ratio (effect size)..: 0.965 
Lower 95% CI..............: 0.807 
Upper 95% CI..............: 1.155 
Z-value...................: -0.38892 
P-value...................: 0.6973356 
Hosmer and Lemeshow r^2...: 0.08514 
Cox and Snell r^2.........: 0.053256 
Nagelkerke's pseudo r^2...: 0.112314 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of LIX1.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age              ORdate_epoch     SmokerStatusEx-smoker  
               0.7730749                -0.2344357                 0.0397499                 0.0002345                -0.6495882  
SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.9072671                -0.7173461                -0.0561449  

Degrees of Freedom: 955 Total (i.e. Null);  948 Residual
Null Deviance:      614.5 
Residual Deviance: 573.7    AIC: 589.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.741e+01  1.933e+03   0.009  0.99281   
currentDF[, TARGET]       -2.658e-01  1.257e-01  -2.114  0.03449 * 
Age                        4.407e-02  1.509e-02   2.920  0.00350 **
Gendermale                -3.676e-01  2.783e-01  -1.321  0.18653   
ORdate_epoch               1.685e-04  9.305e-05   1.810  0.07022 . 
Hypertension.compositeyes -4.705e-01  4.209e-01  -1.118  0.26357   
DiabetesStatusDiabetes    -1.687e-01  2.639e-01  -0.639  0.52260   
SmokerStatusEx-smoker     -5.695e-01  2.770e-01  -2.056  0.03982 * 
SmokerStatusNever smoked  -9.157e-01  3.515e-01  -2.605  0.00918 **
Med.Statin.LLDyes         -1.060e-02  2.963e-01  -0.036  0.97147   
Med.all.antiplateletyes   -7.829e-01  4.882e-01  -1.604  0.10880   
GFR_MDRD                   5.144e-03  6.060e-03   0.849  0.39595   
BMI                       -5.409e-02  2.897e-02  -1.867  0.06191 . 
MedHx_CVDyes              -1.221e-01  2.367e-01  -0.516  0.60583   
stenose50-70%             -1.349e+01  1.933e+03  -0.007  0.99443   
stenose70-90%             -1.542e+01  1.933e+03  -0.008  0.99363   
stenose90-99%             -1.541e+01  1.933e+03  -0.008  0.99364   
stenose100% (Occlusion)   -1.589e+01  1.933e+03  -0.008  0.99344   
stenose50-99%             -7.459e-01  2.756e+03   0.000  0.99978   
stenose70-99%             -5.895e-01  2.107e+03   0.000  0.99978   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 557.82  on 936  degrees of freedom
AIC: 597.82

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LIX1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LIX1 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.265757 
Standard error............: 0.125697 
Odds ratio (effect size)..: 0.767 
Lower 95% CI..............: 0.599 
Upper 95% CI..............: 0.981 
Z-value...................: -2.114261 
P-value...................: 0.03449302 
Hosmer and Lemeshow r^2...: 0.092238 
Cox and Snell r^2.........: 0.057565 
Nagelkerke's pseudo r^2...: 0.121402 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of IGSF9B.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.466e+01  1.963e+03   0.007  0.99404   
currentDF[, TARGET]       -7.838e-02  9.223e-02  -0.850  0.39539   
Age                        4.437e-02  1.514e-02   2.931  0.00338 **
Gendermale                -3.668e-01  2.787e-01  -1.316  0.18809   
ORdate_epoch               2.390e-04  9.180e-05   2.604  0.00922 **
Hypertension.compositeyes -4.480e-01  4.199e-01  -1.067  0.28604   
DiabetesStatusDiabetes    -1.874e-01  2.630e-01  -0.712  0.47619   
SmokerStatusEx-smoker     -5.603e-01  2.765e-01  -2.027  0.04267 * 
SmokerStatusNever smoked  -9.072e-01  3.506e-01  -2.588  0.00967 **
Med.Statin.LLDyes         -1.959e-02  2.969e-01  -0.066  0.94740   
Med.all.antiplateletyes   -7.651e-01  4.874e-01  -1.570  0.11647   
GFR_MDRD                   5.472e-03  6.097e-03   0.897  0.36949   
BMI                       -5.691e-02  2.908e-02  -1.957  0.05038 . 
MedHx_CVDyes              -1.117e-01  2.362e-01  -0.473  0.63614   
stenose50-70%             -1.351e+01  1.963e+03  -0.007  0.99451   
stenose70-90%             -1.533e+01  1.963e+03  -0.008  0.99377   
stenose90-99%             -1.534e+01  1.963e+03  -0.008  0.99376   
stenose100% (Occlusion)   -1.581e+01  1.963e+03  -0.008  0.99357   
stenose50-99%             -6.879e-01  2.780e+03   0.000  0.99980   
stenose70-99%             -4.824e-01  2.134e+03   0.000  0.99982   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 561.62  on 936  degrees of freedom
AIC: 601.62

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' IGSF9B ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: IGSF9B 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.078384 
Standard error............: 0.092228 
Odds ratio (effect size)..: 0.925 
Lower 95% CI..............: 0.772 
Upper 95% CI..............: 1.108 
Z-value...................: -0.849887 
P-value...................: 0.3953877 
Hosmer and Lemeshow r^2...: 0.086055 
Cox and Snell r^2.........: 0.053812 
Nagelkerke's pseudo r^2...: 0.113487 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of ALB.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age                Gendermale              ORdate_epoch  
               0.0677165                -0.2272779                 0.0398313                -0.3812062                 0.0002743  
   SmokerStatusEx-smoker  SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.5942093                -0.9239941                -0.7406772                -0.0582868  

Degrees of Freedom: 955 Total (i.e. Null);  947 Residual
Null Deviance:      614.5 
Residual Deviance: 571.6    AIC: 589.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.567e+01  1.950e+03   0.008  0.99359   
currentDF[, TARGET]       -2.102e-01  1.158e-01  -1.815  0.06946 . 
Age                        4.430e-02  1.517e-02   2.919  0.00351 **
Gendermale                -3.961e-01  2.799e-01  -1.415  0.15696   
ORdate_epoch               2.165e-04  8.950e-05   2.418  0.01559 * 
Hypertension.compositeyes -4.403e-01  4.192e-01  -1.050  0.29365   
DiabetesStatusDiabetes    -1.222e-01  2.656e-01  -0.460  0.64545   
SmokerStatusEx-smoker     -5.768e-01  2.774e-01  -2.079  0.03761 * 
SmokerStatusNever smoked  -9.342e-01  3.523e-01  -2.652  0.00801 **
Med.Statin.LLDyes         -1.672e-02  2.970e-01  -0.056  0.95509   
Med.all.antiplateletyes   -7.782e-01  4.877e-01  -1.596  0.11054   
GFR_MDRD                   5.346e-03  6.102e-03   0.876  0.38097   
BMI                       -5.384e-02  2.910e-02  -1.850  0.06432 . 
MedHx_CVDyes              -1.082e-01  2.364e-01  -0.458  0.64714   
stenose50-70%             -1.343e+01  1.950e+03  -0.007  0.99451   
stenose70-90%             -1.524e+01  1.950e+03  -0.008  0.99376   
stenose90-99%             -1.524e+01  1.950e+03  -0.008  0.99376   
stenose100% (Occlusion)   -1.553e+01  1.950e+03  -0.008  0.99365   
stenose50-99%             -5.137e-01  2.771e+03   0.000  0.99985   
stenose70-99%             -3.870e-01  2.119e+03   0.000  0.99985   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 558.95  on 936  degrees of freedom
AIC: 598.95

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ALB ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ALB 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.210188 
Standard error............: 0.11578 
Odds ratio (effect size)..: 0.81 
Lower 95% CI..............: 0.646 
Upper 95% CI..............: 1.017 
Z-value...................: -1.815403 
P-value...................: 0.06946194 
Hosmer and Lemeshow r^2...: 0.090396 
Cox and Snell r^2.........: 0.056449 
Nagelkerke's pseudo r^2...: 0.119047 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of OR10A4.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age              ORdate_epoch     SmokerStatusEx-smoker  
              -0.5649544                -0.1880573                 0.0404469                 0.0002696                -0.6571629  
SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.9022772                -0.7132984                -0.0569631  

Degrees of Freedom: 955 Total (i.e. Null);  948 Residual
Null Deviance:      614.5 
Residual Deviance: 573.9    AIC: 589.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.540e+01  1.962e+03   0.008  0.99374   
currentDF[, TARGET]       -1.788e-01  1.063e-01  -1.682  0.09263 . 
Age                        4.430e-02  1.516e-02   2.922  0.00348 **
Gendermale                -3.888e-01  2.796e-01  -1.391  0.16431   
ORdate_epoch               2.121e-04  8.973e-05   2.364  0.01810 * 
Hypertension.compositeyes -4.382e-01  4.193e-01  -1.045  0.29601   
DiabetesStatusDiabetes    -1.320e-01  2.652e-01  -0.498  0.61858   
SmokerStatusEx-smoker     -5.752e-01  2.771e-01  -2.076  0.03788 * 
SmokerStatusNever smoked  -9.136e-01  3.515e-01  -2.599  0.00934 **
Med.Statin.LLDyes         -9.240e-03  2.966e-01  -0.031  0.97514   
Med.all.antiplateletyes   -7.777e-01  4.871e-01  -1.597  0.11032   
GFR_MDRD                   5.394e-03  6.101e-03   0.884  0.37659   
BMI                       -5.352e-02  2.912e-02  -1.838  0.06602 . 
MedHx_CVDyes              -1.052e-01  2.364e-01  -0.445  0.65623   
stenose50-70%             -1.342e+01  1.962e+03  -0.007  0.99454   
stenose70-90%             -1.523e+01  1.962e+03  -0.008  0.99380   
stenose90-99%             -1.522e+01  1.962e+03  -0.008  0.99381   
stenose100% (Occlusion)   -1.555e+01  1.962e+03  -0.008  0.99368   
stenose50-99%             -5.325e-01  2.779e+03   0.000  0.99985   
stenose70-99%             -4.053e-01  2.131e+03   0.000  0.99985   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 559.43  on 936  degrees of freedom
AIC: 599.43

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR10A4 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR10A4 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.178783 
Standard error............: 0.106312 
Odds ratio (effect size)..: 0.836 
Lower 95% CI..............: 0.679 
Upper 95% CI..............: 1.03 
Z-value...................: -1.681671 
P-value...................: 0.09263263 
Hosmer and Lemeshow r^2...: 0.089613 
Cox and Snell r^2.........: 0.055974 
Nagelkerke's pseudo r^2...: 0.118045 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of RASEF.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.504e+01  1.951e+03   0.008  0.99385   
currentDF[, TARGET]       -1.026e-01  1.095e-01  -0.937  0.34860   
Age                        4.399e-02  1.513e-02   2.907  0.00364 **
Gendermale                -3.800e-01  2.797e-01  -1.358  0.17431   
ORdate_epoch               2.299e-04  8.984e-05   2.558  0.01052 * 
Hypertension.compositeyes -4.607e-01  4.192e-01  -1.099  0.27186   
DiabetesStatusDiabetes    -1.642e-01  2.634e-01  -0.624  0.53292   
SmokerStatusEx-smoker     -5.618e-01  2.764e-01  -2.032  0.04210 * 
SmokerStatusNever smoked  -9.079e-01  3.507e-01  -2.589  0.00964 **
Med.Statin.LLDyes         -2.393e-02  2.972e-01  -0.081  0.93583   
Med.all.antiplateletyes   -7.771e-01  4.881e-01  -1.592  0.11134   
GFR_MDRD                   5.300e-03  6.101e-03   0.869  0.38500   
BMI                       -5.570e-02  2.895e-02  -1.924  0.05436 . 
MedHx_CVDyes              -1.042e-01  2.360e-01  -0.441  0.65885   
stenose50-70%             -1.343e+01  1.951e+03  -0.007  0.99451   
stenose70-90%             -1.525e+01  1.951e+03  -0.008  0.99376   
stenose90-99%             -1.526e+01  1.951e+03  -0.008  0.99376   
stenose100% (Occlusion)   -1.569e+01  1.951e+03  -0.008  0.99358   
stenose50-99%             -6.203e-01  2.772e+03   0.000  0.99982   
stenose70-99%             -4.705e-01  2.124e+03   0.000  0.99982   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 561.43  on 936  degrees of freedom
AIC: 601.43

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' RASEF ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: RASEF 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.10263 
Standard error............: 0.109493 
Odds ratio (effect size)..: 0.902 
Lower 95% CI..............: 0.728 
Upper 95% CI..............: 1.118 
Z-value...................: -0.937316 
P-value...................: 0.3485959 
Hosmer and Lemeshow r^2...: 0.086353 
Cox and Snell r^2.........: 0.053994 
Nagelkerke's pseudo r^2...: 0.11387 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of NEDD4.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.520e+01  1.956e+03   0.008  0.99380   
currentDF[, TARGET]       -9.819e-02  1.163e-01  -0.844  0.39856   
Age                        4.370e-02  1.512e-02   2.890  0.00385 **
Gendermale                -3.654e-01  2.785e-01  -1.312  0.18959   
ORdate_epoch               2.258e-04  8.943e-05   2.525  0.01158 * 
Hypertension.compositeyes -4.636e-01  4.191e-01  -1.106  0.26859   
DiabetesStatusDiabetes    -1.648e-01  2.634e-01  -0.626  0.53154   
SmokerStatusEx-smoker     -5.685e-01  2.765e-01  -2.056  0.03977 * 
SmokerStatusNever smoked  -9.065e-01  3.506e-01  -2.585  0.00972 **
Med.Statin.LLDyes         -2.437e-02  2.972e-01  -0.082  0.93466   
Med.all.antiplateletyes   -7.615e-01  4.872e-01  -1.563  0.11805   
GFR_MDRD                   5.187e-03  6.108e-03   0.849  0.39575   
BMI                       -5.470e-02  2.896e-02  -1.889  0.05893 . 
MedHx_CVDyes              -1.031e-01  2.360e-01  -0.437  0.66208   
stenose50-70%             -1.346e+01  1.956e+03  -0.007  0.99451   
stenose70-90%             -1.528e+01  1.956e+03  -0.008  0.99377   
stenose90-99%             -1.528e+01  1.956e+03  -0.008  0.99376   
stenose100% (Occlusion)   -1.572e+01  1.956e+03  -0.008  0.99359   
stenose50-99%             -6.110e-01  2.775e+03   0.000  0.99982   
stenose70-99%             -4.892e-01  2.128e+03   0.000  0.99982   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 561.61  on 936  degrees of freedom
AIC: 601.61

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NEDD4 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NEDD4 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.09819 
Standard error............: 0.116311 
Odds ratio (effect size)..: 0.906 
Lower 95% CI..............: 0.722 
Upper 95% CI..............: 1.139 
Z-value...................: -0.844199 
P-value...................: 0.3985581 
Hosmer and Lemeshow r^2...: 0.086073 
Cox and Snell r^2.........: 0.053823 
Nagelkerke's pseudo r^2...: 0.11351 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of TCL1A.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age              ORdate_epoch     SmokerStatusEx-smoker  
               0.9293438                -0.2315396                 0.0395303                 0.0002328                -0.6460060  
SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.8964819                -0.6919319                -0.0568670  

Degrees of Freedom: 955 Total (i.e. Null);  948 Residual
Null Deviance:      614.5 
Residual Deviance: 574.3    AIC: 590.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.737e+01  1.944e+03   0.009  0.99287   
currentDF[, TARGET]       -2.570e-01  1.351e-01  -1.901  0.05724 . 
Age                        4.362e-02  1.508e-02   2.893  0.00382 **
Gendermale                -3.591e-01  2.779e-01  -1.292  0.19634   
ORdate_epoch               1.690e-04  9.376e-05   1.802  0.07147 . 
Hypertension.compositeyes -4.813e-01  4.208e-01  -1.144  0.25275   
DiabetesStatusDiabetes    -1.741e-01  2.639e-01  -0.660  0.50948   
SmokerStatusEx-smoker     -5.649e-01  2.767e-01  -2.042  0.04119 * 
SmokerStatusNever smoked  -8.967e-01  3.510e-01  -2.555  0.01063 * 
Med.Statin.LLDyes         -2.311e-02  2.960e-01  -0.078  0.93777   
Med.all.antiplateletyes   -7.540e-01  4.876e-01  -1.546  0.12205   
GFR_MDRD                   5.214e-03  6.062e-03   0.860  0.38967   
BMI                       -5.461e-02  2.892e-02  -1.888  0.05897 . 
MedHx_CVDyes              -1.076e-01  2.364e-01  -0.455  0.64900   
stenose50-70%             -1.338e+01  1.944e+03  -0.007  0.99451   
stenose70-90%             -1.529e+01  1.944e+03  -0.008  0.99372   
stenose90-99%             -1.527e+01  1.944e+03  -0.008  0.99373   
stenose100% (Occlusion)   -1.576e+01  1.944e+03  -0.008  0.99353   
stenose50-99%             -6.310e-01  2.765e+03   0.000  0.99982   
stenose70-99%             -4.838e-01  2.118e+03   0.000  0.99982   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.5  on 955  degrees of freedom
Residual deviance: 558.7  on 936  degrees of freedom
AIC: 598.7

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TCL1A ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TCL1A 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.256969 
Standard error............: 0.135142 
Odds ratio (effect size)..: 0.773 
Lower 95% CI..............: 0.593 
Upper 95% CI..............: 1.008 
Z-value...................: -1.901467 
P-value...................: 0.05724087 
Hosmer and Lemeshow r^2...: 0.090801 
Cox and Snell r^2.........: 0.056694 
Nagelkerke's pseudo r^2...: 0.119565 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of FBXO15.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.568e+01  1.955e+03   0.008  0.99360   
currentDF[, TARGET]       -1.444e-01  1.184e-01  -1.219  0.22271   
Age                        4.400e-02  1.514e-02   2.906  0.00366 **
Gendermale                -3.843e-01  2.796e-01  -1.374  0.16931   
ORdate_epoch               2.177e-04  8.943e-05   2.434  0.01492 * 
Hypertension.compositeyes -4.554e-01  4.191e-01  -1.087  0.27720   
DiabetesStatusDiabetes    -1.487e-01  2.643e-01  -0.563  0.57365   
SmokerStatusEx-smoker     -5.734e-01  2.768e-01  -2.071  0.03833 * 
SmokerStatusNever smoked  -9.184e-01  3.513e-01  -2.614  0.00895 **
Med.Statin.LLDyes         -2.083e-02  2.969e-01  -0.070  0.94406   
Med.all.antiplateletyes   -7.689e-01  4.874e-01  -1.578  0.11467   
GFR_MDRD                   5.300e-03  6.100e-03   0.869  0.38493   
BMI                       -5.461e-02  2.902e-02  -1.882  0.05985 . 
MedHx_CVDyes              -1.049e-01  2.360e-01  -0.444  0.65675   
stenose50-70%             -1.345e+01  1.955e+03  -0.007  0.99451   
stenose70-90%             -1.527e+01  1.955e+03  -0.008  0.99377   
stenose90-99%             -1.526e+01  1.955e+03  -0.008  0.99377   
stenose100% (Occlusion)   -1.564e+01  1.955e+03  -0.008  0.99362   
stenose50-99%             -5.725e-01  2.776e+03   0.000  0.99984   
stenose70-99%             -4.434e-01  2.126e+03   0.000  0.99983   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.5  on 955  degrees of freedom
Residual deviance: 560.8  on 936  degrees of freedom
AIC: 600.8

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FBXO15 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FBXO15 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.1444 
Standard error............: 0.118424 
Odds ratio (effect size)..: 0.866 
Lower 95% CI..............: 0.686 
Upper 95% CI..............: 1.092 
Z-value...................: -1.219346 
P-value...................: 0.2227128 
Hosmer and Lemeshow r^2...: 0.087391 
Cox and Snell r^2.........: 0.054625 
Nagelkerke's pseudo r^2...: 0.1152 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of F5.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age                Gendermale              ORdate_epoch  
              -0.0404862                -0.2307456                 0.0397890                -0.3822379                 0.0002883  
   SmokerStatusEx-smoker  SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.5741932                -0.9019022                -0.7668709                -0.0591524  

Degrees of Freedom: 955 Total (i.e. Null);  947 Residual
Null Deviance:      614.5 
Residual Deviance: 571.9    AIC: 589.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.561e+01  1.953e+03   0.008  0.99362   
currentDF[, TARGET]       -2.082e-01  1.196e-01  -1.741  0.08173 . 
Age                        4.386e-02  1.513e-02   2.900  0.00374 **
Gendermale                -3.943e-01  2.795e-01  -1.411  0.15830   
ORdate_epoch               2.327e-04  8.935e-05   2.604  0.00920 **
Hypertension.compositeyes -4.667e-01  4.194e-01  -1.113  0.26584   
DiabetesStatusDiabetes    -1.533e-01  2.642e-01  -0.580  0.56187   
SmokerStatusEx-smoker     -5.581e-01  2.770e-01  -2.015  0.04391 * 
SmokerStatusNever smoked  -9.084e-01  3.515e-01  -2.585  0.00975 **
Med.Statin.LLDyes         -3.501e-02  2.979e-01  -0.118  0.90644   
Med.all.antiplateletyes   -7.966e-01  4.890e-01  -1.629  0.10326   
GFR_MDRD                   5.012e-03  6.121e-03   0.819  0.41287   
BMI                       -5.429e-02  2.914e-02  -1.863  0.06248 . 
MedHx_CVDyes              -1.075e-01  2.362e-01  -0.455  0.64882   
stenose50-70%             -1.348e+01  1.953e+03  -0.007  0.99449   
stenose70-90%             -1.528e+01  1.953e+03  -0.008  0.99376   
stenose90-99%             -1.529e+01  1.953e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.564e+01  1.953e+03  -0.008  0.99361   
stenose50-99%             -5.442e-01  2.774e+03   0.000  0.99984   
stenose70-99%             -5.157e-01  2.124e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 559.22  on 936  degrees of freedom
AIC: 599.22

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' F5 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: F5 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.208239 
Standard error............: 0.119627 
Odds ratio (effect size)..: 0.812 
Lower 95% CI..............: 0.642 
Upper 95% CI..............: 1.027 
Z-value...................: -1.740742 
P-value...................: 0.08172887 
Hosmer and Lemeshow r^2...: 0.089955 
Cox and Snell r^2.........: 0.056181 
Nagelkerke's pseudo r^2...: 0.118483 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of TMEM212.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age              ORdate_epoch     SmokerStatusEx-smoker  
              -0.2032754                -0.1909839                 0.0403022                 0.0002795                -0.6502201  
SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.9088212                -0.7174663                -0.0573126  

Degrees of Freedom: 955 Total (i.e. Null);  948 Residual
Null Deviance:      614.5 
Residual Deviance: 574.7    AIC: 590.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.578e+01  1.951e+03   0.008  0.99355   
currentDF[, TARGET]       -1.782e-01  1.229e-01  -1.449  0.14727   
Age                        4.375e-02  1.514e-02   2.890  0.00386 **
Gendermale                -3.841e-01  2.791e-01  -1.376  0.16880   
ORdate_epoch               2.237e-04  8.936e-05   2.504  0.01229 * 
Hypertension.compositeyes -4.577e-01  4.191e-01  -1.092  0.27472   
DiabetesStatusDiabetes    -1.377e-01  2.649e-01  -0.520  0.60312   
SmokerStatusEx-smoker     -5.656e-01  2.767e-01  -2.044  0.04096 * 
SmokerStatusNever smoked  -9.156e-01  3.512e-01  -2.607  0.00914 **
Med.Statin.LLDyes         -2.816e-02  2.971e-01  -0.095  0.92449   
Med.all.antiplateletyes   -7.748e-01  4.875e-01  -1.589  0.11200   
GFR_MDRD                   5.063e-03  6.103e-03   0.830  0.40673   
BMI                       -5.408e-02  2.907e-02  -1.860  0.06282 . 
MedHx_CVDyes              -1.122e-01  2.362e-01  -0.475  0.63474   
stenose50-70%             -1.342e+01  1.951e+03  -0.007  0.99451   
stenose70-90%             -1.524e+01  1.951e+03  -0.008  0.99377   
stenose90-99%             -1.524e+01  1.951e+03  -0.008  0.99377   
stenose100% (Occlusion)   -1.560e+01  1.951e+03  -0.008  0.99362   
stenose50-99%             -5.473e-01  2.772e+03   0.000  0.99984   
stenose70-99%             -4.434e-01  2.123e+03   0.000  0.99983   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 560.16  on 936  degrees of freedom
AIC: 600.16

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TMEM212 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TMEM212 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.178163 
Standard error............: 0.122935 
Odds ratio (effect size)..: 0.837 
Lower 95% CI..............: 0.658 
Upper 95% CI..............: 1.065 
Z-value...................: -1.449243 
P-value...................: 0.1472698 
Hosmer and Lemeshow r^2...: 0.088422 
Cox and Snell r^2.........: 0.055251 
Nagelkerke's pseudo r^2...: 0.116522 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of PTPRD.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.449e+01  1.956e+03   0.007  0.99409   
currentDF[, TARGET]       -3.322e-02  2.023e-01  -0.164  0.86959   
Age                        4.408e-02  1.510e-02   2.920  0.00351 **
Gendermale                -3.499e-01  2.779e-01  -1.259  0.20804   
ORdate_epoch               2.219e-04  8.970e-05   2.474  0.01338 * 
Hypertension.compositeyes -4.698e-01  4.191e-01  -1.121  0.26234   
DiabetesStatusDiabetes    -1.794e-01  2.627e-01  -0.683  0.49468   
SmokerStatusEx-smoker     -5.650e-01  2.762e-01  -2.046  0.04078 * 
SmokerStatusNever smoked  -8.999e-01  3.499e-01  -2.571  0.01013 * 
Med.Statin.LLDyes         -5.935e-03  2.957e-01  -0.020  0.98399   
Med.all.antiplateletyes   -7.507e-01  4.869e-01  -1.542  0.12316   
GFR_MDRD                   5.606e-03  6.075e-03   0.923  0.35606   
BMI                       -5.471e-02  2.905e-02  -1.884  0.05962 . 
MedHx_CVDyes              -1.057e-01  2.362e-01  -0.447  0.65455   
stenose50-70%             -1.349e+01  1.956e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.956e+03  -0.008  0.99375   
stenose90-99%             -1.533e+01  1.956e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.580e+01  1.956e+03  -0.008  0.99356   
stenose50-99%             -7.188e-01  2.775e+03   0.000  0.99979   
stenose70-99%             -4.952e-01  2.129e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.5  on 955  degrees of freedom
Residual deviance: 562.3  on 936  degrees of freedom
AIC: 602.3

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTPRD ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTPRD 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.033221 
Standard error............: 0.20235 
Odds ratio (effect size)..: 0.967 
Lower 95% CI..............: 0.651 
Upper 95% CI..............: 1.438 
Z-value...................: -0.164176 
P-value...................: 0.8695924 
Hosmer and Lemeshow r^2...: 0.08494 
Cox and Snell r^2.........: 0.053134 
Nagelkerke's pseudo r^2...: 0.112057 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of CYP46A1.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age              ORdate_epoch     SmokerStatusEx-smoker  
               0.0143982                -0.2056683                 0.0409718                 0.0002702                -0.6604869  
SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.9071476                -0.7079757                -0.0568934  

Degrees of Freedom: 955 Total (i.e. Null);  948 Residual
Null Deviance:      614.5 
Residual Deviance: 574.2    AIC: 590.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.599e+01  1.951e+03   0.008  0.99346   
currentDF[, TARGET]       -1.969e-01  1.229e-01  -1.601  0.10931   
Age                        4.452e-02  1.518e-02   2.932  0.00337 **
Gendermale                -3.818e-01  2.791e-01  -1.368  0.17137   
ORdate_epoch               2.138e-04  8.975e-05   2.382  0.01721 * 
Hypertension.compositeyes -4.467e-01  4.191e-01  -1.066  0.28655   
DiabetesStatusDiabetes    -1.450e-01  2.643e-01  -0.548  0.58339   
SmokerStatusEx-smoker     -5.783e-01  2.772e-01  -2.086  0.03698 * 
SmokerStatusNever smoked  -9.184e-01  3.514e-01  -2.613  0.00896 **
Med.Statin.LLDyes         -2.486e-02  2.971e-01  -0.084  0.93331   
Med.all.antiplateletyes   -7.672e-01  4.873e-01  -1.574  0.11541   
GFR_MDRD                   5.196e-03  6.095e-03   0.852  0.39395   
BMI                       -5.335e-02  2.906e-02  -1.836  0.06642 . 
MedHx_CVDyes              -1.044e-01  2.361e-01  -0.442  0.65838   
stenose50-70%             -1.342e+01  1.951e+03  -0.007  0.99451   
stenose70-90%             -1.523e+01  1.951e+03  -0.008  0.99377   
stenose90-99%             -1.523e+01  1.951e+03  -0.008  0.99377   
stenose100% (Occlusion)   -1.555e+01  1.951e+03  -0.008  0.99364   
stenose50-99%             -5.251e-01  2.772e+03   0.000  0.99985   
stenose70-99%             -4.062e-01  2.121e+03   0.000  0.99985   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 559.64  on 936  degrees of freedom
AIC: 599.64

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CYP46A1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CYP46A1 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.196877 
Standard error............: 0.122949 
Odds ratio (effect size)..: 0.821 
Lower 95% CI..............: 0.645 
Upper 95% CI..............: 1.045 
Z-value...................: -1.60129 
P-value...................: 0.1093126 
Hosmer and Lemeshow r^2...: 0.089279 
Cox and Snell r^2.........: 0.055771 
Nagelkerke's pseudo r^2...: 0.117618 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of OR2T33.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age              ORdate_epoch     SmokerStatusEx-smoker  
              -0.5576402                -0.1759379                 0.0401173                 0.0002835                -0.6532403  
SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.9087687                -0.7079504                -0.0578091  

Degrees of Freedom: 955 Total (i.e. Null);  948 Residual
Null Deviance:      614.5 
Residual Deviance: 574.8    AIC: 590.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.541e+01  1.952e+03   0.008  0.99370   
currentDF[, TARGET]       -1.611e-01  1.157e-01  -1.393  0.16364   
Age                        4.354e-02  1.516e-02   2.871  0.00409 **
Gendermale                -3.762e-01  2.788e-01  -1.350  0.17717   
ORdate_epoch               2.280e-04  8.954e-05   2.547  0.01087 * 
Hypertension.compositeyes -4.622e-01  4.191e-01  -1.103  0.27004   
DiabetesStatusDiabetes    -1.449e-01  2.645e-01  -0.548  0.58387   
SmokerStatusEx-smoker     -5.710e-01  2.767e-01  -2.064  0.03903 * 
SmokerStatusNever smoked  -9.136e-01  3.511e-01  -2.602  0.00927 **
Med.Statin.LLDyes         -2.768e-02  2.971e-01  -0.093  0.92578   
Med.all.antiplateletyes   -7.661e-01  4.874e-01  -1.572  0.11602   
GFR_MDRD                   4.984e-03  6.108e-03   0.816  0.41451   
BMI                       -5.439e-02  2.899e-02  -1.876  0.06061 . 
MedHx_CVDyes              -1.067e-01  2.362e-01  -0.452  0.65136   
stenose50-70%             -1.340e+01  1.952e+03  -0.007  0.99452   
stenose70-90%             -1.522e+01  1.952e+03  -0.008  0.99378   
stenose90-99%             -1.522e+01  1.952e+03  -0.008  0.99378   
stenose100% (Occlusion)   -1.559e+01  1.952e+03  -0.008  0.99363   
stenose50-99%             -5.541e-01  2.772e+03   0.000  0.99984   
stenose70-99%             -4.504e-01  2.124e+03   0.000  0.99983   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 560.33  on 936  degrees of freedom
AIC: 600.33

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' OR2T33 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: OR2T33 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.161123 
Standard error............: 0.115672 
Odds ratio (effect size)..: 0.851 
Lower 95% CI..............: 0.679 
Upper 95% CI..............: 1.068 
Z-value...................: -1.392927 
P-value...................: 0.163642 
Hosmer and Lemeshow r^2...: 0.088154 
Cox and Snell r^2.........: 0.055088 
Nagelkerke's pseudo r^2...: 0.116178 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of SORBS2.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.495e+01  1.964e+03   0.008  0.99393   
currentDF[, TARGET]       -1.054e-01  1.061e-01  -0.993  0.32048   
Age                        4.442e-02  1.515e-02   2.933  0.00336 **
Gendermale                -3.741e-01  2.795e-01  -1.338  0.18084   
ORdate_epoch               2.309e-04  8.956e-05   2.578  0.00993 **
Hypertension.compositeyes -4.421e-01  4.201e-01  -1.053  0.29257   
DiabetesStatusDiabetes    -1.881e-01  2.632e-01  -0.715  0.47475   
SmokerStatusEx-smoker     -5.555e-01  2.769e-01  -2.006  0.04487 * 
SmokerStatusNever smoked  -8.806e-01  3.509e-01  -2.509  0.01210 * 
Med.Statin.LLDyes         -1.454e-02  2.960e-01  -0.049  0.96082   
Med.all.antiplateletyes   -7.389e-01  4.866e-01  -1.518  0.12891   
GFR_MDRD                   5.807e-03  6.112e-03   0.950  0.34202   
BMI                       -5.273e-02  2.917e-02  -1.807  0.07071 . 
MedHx_CVDyes              -1.157e-01  2.367e-01  -0.489  0.62494   
stenose50-70%             -1.351e+01  1.964e+03  -0.007  0.99451   
stenose70-90%             -1.534e+01  1.964e+03  -0.008  0.99377   
stenose90-99%             -1.535e+01  1.964e+03  -0.008  0.99376   
stenose100% (Occlusion)   -1.580e+01  1.964e+03  -0.008  0.99358   
stenose50-99%             -6.728e-01  2.780e+03   0.000  0.99981   
stenose70-99%             -4.724e-01  2.135e+03   0.000  0.99982   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 561.36  on 936  degrees of freedom
AIC: 601.36

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SORBS2 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SORBS2 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.105383 
Standard error............: 0.106076 
Odds ratio (effect size)..: 0.9 
Lower 95% CI..............: 0.731 
Upper 95% CI..............: 1.108 
Z-value...................: -0.993463 
P-value...................: 0.3204843 
Hosmer and Lemeshow r^2...: 0.086472 
Cox and Snell r^2.........: 0.054066 
Nagelkerke's pseudo r^2...: 0.114021 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of ITGA7.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.501e+01  1.958e+03   0.008  0.99388   
currentDF[, TARGET]       -8.674e-02  1.333e-01  -0.651  0.51512   
Age                        4.397e-02  1.511e-02   2.910  0.00361 **
Gendermale                -3.645e-01  2.788e-01  -1.307  0.19109   
ORdate_epoch               2.276e-04  8.968e-05   2.538  0.01114 * 
Hypertension.compositeyes -4.619e-01  4.192e-01  -1.102  0.27053   
DiabetesStatusDiabetes    -1.698e-01  2.632e-01  -0.645  0.51889   
SmokerStatusEx-smoker     -5.682e-01  2.763e-01  -2.057  0.03972 * 
SmokerStatusNever smoked  -9.093e-01  3.507e-01  -2.593  0.00951 **
Med.Statin.LLDyes         -1.833e-02  2.967e-01  -0.062  0.95072   
Med.all.antiplateletyes   -7.608e-01  4.872e-01  -1.562  0.11839   
GFR_MDRD                   5.355e-03  6.103e-03   0.877  0.38026   
BMI                       -5.484e-02  2.898e-02  -1.892  0.05844 . 
MedHx_CVDyes              -1.056e-01  2.360e-01  -0.447  0.65464   
stenose50-70%             -1.349e+01  1.958e+03  -0.007  0.99450   
stenose70-90%             -1.530e+01  1.958e+03  -0.008  0.99376   
stenose90-99%             -1.531e+01  1.958e+03  -0.008  0.99376   
stenose100% (Occlusion)   -1.573e+01  1.958e+03  -0.008  0.99359   
stenose50-99%             -6.326e-01  2.776e+03   0.000  0.99982   
stenose70-99%             -4.814e-01  2.130e+03   0.000  0.99982   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.5  on 955  degrees of freedom
Residual deviance: 561.9  on 936  degrees of freedom
AIC: 601.9

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ITGA7 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ITGA7 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.086737 
Standard error............: 0.133259 
Odds ratio (effect size)..: 0.917 
Lower 95% CI..............: 0.706 
Upper 95% CI..............: 1.191 
Z-value...................: -0.650886 
P-value...................: 0.5151199 
Hosmer and Lemeshow r^2...: 0.08559 
Cox and Snell r^2.........: 0.053529 
Nagelkerke's pseudo r^2...: 0.112891 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of FOS.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.435e+01  1.955e+03   0.007  0.99414   
currentDF[, TARGET]        1.062e-02  6.539e-02   0.162  0.87097   
Age                        4.421e-02  1.510e-02   2.927  0.00342 **
Gendermale                -3.517e-01  2.781e-01  -1.265  0.20591   
ORdate_epoch               2.176e-04  9.093e-05   2.393  0.01673 * 
Hypertension.compositeyes -4.738e-01  4.199e-01  -1.129  0.25909   
DiabetesStatusDiabetes    -1.802e-01  2.628e-01  -0.686  0.49277   
SmokerStatusEx-smoker     -5.671e-01  2.760e-01  -2.054  0.03996 * 
SmokerStatusNever smoked  -9.011e-01  3.502e-01  -2.573  0.01008 * 
Med.Statin.LLDyes         -7.270e-03  2.958e-01  -0.025  0.98040   
Med.all.antiplateletyes   -7.521e-01  4.867e-01  -1.545  0.12229   
GFR_MDRD                   5.565e-03  6.080e-03   0.915  0.36004   
BMI                       -5.516e-02  2.903e-02  -1.900  0.05745 . 
MedHx_CVDyes              -1.029e-01  2.362e-01  -0.436  0.66318   
stenose50-70%             -1.348e+01  1.955e+03  -0.007  0.99450   
stenose70-90%             -1.531e+01  1.955e+03  -0.008  0.99375   
stenose90-99%             -1.532e+01  1.955e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.579e+01  1.955e+03  -0.008  0.99356   
stenose50-99%             -7.334e-01  2.775e+03   0.000  0.99979   
stenose70-99%             -5.003e-01  2.128e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.5  on 955  degrees of freedom
Residual deviance: 562.3  on 936  degrees of freedom
AIC: 602.3

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FOS ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FOS 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.010622 
Standard error............: 0.065395 
Odds ratio (effect size)..: 1.011 
Lower 95% CI..............: 0.889 
Upper 95% CI..............: 1.149 
Z-value...................: 0.162432 
P-value...................: 0.870966 
Hosmer and Lemeshow r^2...: 0.084939 
Cox and Snell r^2.........: 0.053133 
Nagelkerke's pseudo r^2...: 0.112055 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of HMOX1.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + ORdate_epoch + SmokerStatus + Med.all.antiplatelet + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
             (Intercept)       currentDF[, TARGET]                       Age              ORdate_epoch     SmokerStatusEx-smoker  
              -2.9440211                 0.1915015                 0.0402995                 0.0002671                -0.6585733  
SmokerStatusNever smoked   Med.all.antiplateletyes                       BMI  
              -0.9111269                -0.7019543                -0.0588478  

Degrees of Freedom: 955 Total (i.e. Null);  948 Residual
Null Deviance:      614.5 
Residual Deviance: 574.2    AIC: 590.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.299e+01  1.923e+03   0.007  0.99461   
currentDF[, TARGET]        2.043e-01  1.144e-01   1.786  0.07411 . 
Age                        4.401e-02  1.508e-02   2.919  0.00352 **
Gendermale                -3.864e-01  2.785e-01  -1.387  0.16537   
ORdate_epoch               2.123e-04  8.952e-05   2.372  0.01770 * 
Hypertension.compositeyes -4.924e-01  4.195e-01  -1.174  0.24048   
DiabetesStatusDiabetes    -1.814e-01  2.639e-01  -0.688  0.49171   
SmokerStatusEx-smoker     -5.708e-01  2.766e-01  -2.064  0.03906 * 
SmokerStatusNever smoked  -9.132e-01  3.509e-01  -2.602  0.00926 **
Med.Statin.LLDyes         -1.896e-02  2.962e-01  -0.064  0.94895   
Med.all.antiplateletyes   -7.730e-01  4.881e-01  -1.584  0.11325   
GFR_MDRD                   4.986e-03  6.047e-03   0.825  0.40964   
BMI                       -5.573e-02  2.909e-02  -1.915  0.05545 . 
MedHx_CVDyes              -1.049e-01  2.360e-01  -0.444  0.65670   
stenose50-70%             -1.328e+01  1.923e+03  -0.007  0.99449   
stenose70-90%             -1.517e+01  1.923e+03  -0.008  0.99371   
stenose90-99%             -1.514e+01  1.923e+03  -0.008  0.99372   
stenose100% (Occlusion)   -1.547e+01  1.923e+03  -0.008  0.99358   
stenose50-99%             -5.535e-01  2.755e+03   0.000  0.99984   
stenose70-99%             -4.075e-01  2.098e+03   0.000  0.99985   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 558.92  on 936  degrees of freedom
AIC: 598.92

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HMOX1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HMOX1 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.204262 
Standard error............: 0.114373 
Odds ratio (effect size)..: 1.227 
Lower 95% CI..............: 0.98 
Upper 95% CI..............: 1.535 
Z-value...................: 1.785925 
P-value...................: 0.07411135 
Hosmer and Lemeshow r^2...: 0.090448 
Cox and Snell r^2.........: 0.05648 
Nagelkerke's pseudo r^2...: 0.119114 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of LAPTM5.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.440e+01  1.957e+03   0.007  0.99413   
currentDF[, TARGET]       -5.036e-03  5.220e-02  -0.096  0.92314   
Age                        4.409e-02  1.511e-02   2.918  0.00352 **
Gendermale                -3.496e-01  2.780e-01  -1.258  0.20854   
ORdate_epoch               2.209e-04  8.934e-05   2.472  0.01343 * 
Hypertension.compositeyes -4.694e-01  4.192e-01  -1.120  0.26284   
DiabetesStatusDiabetes    -1.793e-01  2.628e-01  -0.682  0.49494   
SmokerStatusEx-smoker     -5.666e-01  2.760e-01  -2.053  0.04005 * 
SmokerStatusNever smoked  -8.975e-01  3.502e-01  -2.563  0.01039 * 
Med.Statin.LLDyes         -4.996e-03  2.959e-01  -0.017  0.98653   
Med.all.antiplateletyes   -7.530e-01  4.868e-01  -1.547  0.12191   
GFR_MDRD                   5.643e-03  6.090e-03   0.927  0.35412   
BMI                       -5.495e-02  2.899e-02  -1.895  0.05805 . 
MedHx_CVDyes              -1.052e-01  2.362e-01  -0.445  0.65598   
stenose50-70%             -1.349e+01  1.957e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.957e+03  -0.008  0.99376   
stenose90-99%             -1.533e+01  1.957e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.581e+01  1.957e+03  -0.008  0.99355   
stenose50-99%             -7.287e-01  2.777e+03   0.000  0.99979   
stenose70-99%             -4.980e-01  2.130e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.32  on 936  degrees of freedom
AIC: 602.32

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LAPTM5 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LAPTM5 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.005036 
Standard error............: 0.052198 
Odds ratio (effect size)..: 0.995 
Lower 95% CI..............: 0.898 
Upper 95% CI..............: 1.102 
Z-value...................: -0.096481 
P-value...................: 0.9231389 
Hosmer and Lemeshow r^2...: 0.084911 
Cox and Snell r^2.........: 0.053116 
Nagelkerke's pseudo r^2...: 0.11202 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of MMP9.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.439e+01  1.957e+03   0.007  0.99413   
currentDF[, TARGET]       -3.163e-03  5.588e-02  -0.057  0.95487   
Age                        4.413e-02  1.510e-02   2.923  0.00347 **
Gendermale                -3.494e-01  2.782e-01  -1.256  0.20918   
ORdate_epoch               2.208e-04  8.971e-05   2.462  0.01383 * 
Hypertension.compositeyes -4.696e-01  4.192e-01  -1.120  0.26262   
DiabetesStatusDiabetes    -1.794e-01  2.627e-01  -0.683  0.49468   
SmokerStatusEx-smoker     -5.671e-01  2.761e-01  -2.054  0.03997 * 
SmokerStatusNever smoked  -8.987e-01  3.500e-01  -2.568  0.01023 * 
Med.Statin.LLDyes         -5.027e-03  2.962e-01  -0.017  0.98646   
Med.all.antiplateletyes   -7.527e-01  4.868e-01  -1.546  0.12208   
GFR_MDRD                   5.628e-03  6.085e-03   0.925  0.35505   
BMI                       -5.500e-02  2.900e-02  -1.896  0.05794 . 
MedHx_CVDyes              -1.046e-01  2.361e-01  -0.443  0.65762   
stenose50-70%             -1.349e+01  1.957e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.957e+03  -0.008  0.99375   
stenose90-99%             -1.533e+01  1.957e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.581e+01  1.957e+03  -0.008  0.99355   
stenose50-99%             -7.313e-01  2.776e+03   0.000  0.99979   
stenose70-99%             -5.021e-01  2.130e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.33  on 936  degrees of freedom
AIC: 602.33

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' MMP9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: MMP9 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.003163 
Standard error............: 0.055879 
Odds ratio (effect size)..: 0.997 
Lower 95% CI..............: 0.893 
Upper 95% CI..............: 1.112 
Z-value...................: -0.056596 
P-value...................: 0.9548672 
Hosmer and Lemeshow r^2...: 0.084901 
Cox and Snell r^2.........: 0.05311 
Nagelkerke's pseudo r^2...: 0.112007 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of SIGLEC1.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.433e+01  1.955e+03   0.007  0.99415   
currentDF[, TARGET]        1.018e-02  6.270e-02   0.162  0.87105   
Age                        4.416e-02  1.510e-02   2.924  0.00345 **
Gendermale                -3.518e-01  2.781e-01  -1.265  0.20577   
ORdate_epoch               2.194e-04  8.945e-05   2.453  0.01418 * 
Hypertension.compositeyes -4.710e-01  4.192e-01  -1.124  0.26122   
DiabetesStatusDiabetes    -1.799e-01  2.628e-01  -0.685  0.49357   
SmokerStatusEx-smoker     -5.678e-01  2.761e-01  -2.057  0.03971 * 
SmokerStatusNever smoked  -9.013e-01  3.502e-01  -2.573  0.01007 * 
Med.Statin.LLDyes         -7.764e-03  2.959e-01  -0.026  0.97907   
Med.all.antiplateletyes   -7.518e-01  4.868e-01  -1.544  0.12253   
GFR_MDRD                   5.536e-03  6.092e-03   0.909  0.36347   
BMI                       -5.497e-02  2.899e-02  -1.896  0.05795 . 
MedHx_CVDyes              -1.022e-01  2.364e-01  -0.432  0.66566   
stenose50-70%             -1.348e+01  1.955e+03  -0.007  0.99450   
stenose70-90%             -1.531e+01  1.955e+03  -0.008  0.99375   
stenose90-99%             -1.532e+01  1.955e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.578e+01  1.955e+03  -0.008  0.99356   
stenose50-99%             -7.249e-01  2.776e+03   0.000  0.99979   
stenose70-99%             -4.984e-01  2.128e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.5  on 955  degrees of freedom
Residual deviance: 562.3  on 936  degrees of freedom
AIC: 602.3

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SIGLEC1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SIGLEC1 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.010178 
Standard error............: 0.0627 
Odds ratio (effect size)..: 1.01 
Lower 95% CI..............: 0.893 
Upper 95% CI..............: 1.142 
Z-value...................: 0.16233 
P-value...................: 0.8710462 
Hosmer and Lemeshow r^2...: 0.084939 
Cox and Snell r^2.........: 0.053133 
Nagelkerke's pseudo r^2...: 0.112055 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of FTL.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.433e+01  1.955e+03   0.007  0.99415   
currentDF[, TARGET]        4.049e-03  5.320e-02   0.076  0.93934   
Age                        4.418e-02  1.511e-02   2.924  0.00345 **
Gendermale                -3.507e-01  2.780e-01  -1.262  0.20710   
ORdate_epoch               2.198e-04  8.951e-05   2.455  0.01409 * 
Hypertension.compositeyes -4.709e-01  4.193e-01  -1.123  0.26146   
DiabetesStatusDiabetes    -1.791e-01  2.628e-01  -0.682  0.49548   
SmokerStatusEx-smoker     -5.665e-01  2.760e-01  -2.053  0.04012 * 
SmokerStatusNever smoked  -8.999e-01  3.501e-01  -2.570  0.01016 * 
Med.Statin.LLDyes         -6.734e-03  2.959e-01  -0.023  0.98184   
Med.all.antiplateletyes   -7.526e-01  4.868e-01  -1.546  0.12210   
GFR_MDRD                   5.593e-03  6.082e-03   0.920  0.35779   
BMI                       -5.487e-02  2.901e-02  -1.891  0.05857 . 
MedHx_CVDyes              -1.036e-01  2.363e-01  -0.438  0.66110   
stenose50-70%             -1.349e+01  1.955e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.955e+03  -0.008  0.99375   
stenose90-99%             -1.533e+01  1.955e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.579e+01  1.955e+03  -0.008  0.99356   
stenose50-99%             -7.242e-01  2.776e+03   0.000  0.99979   
stenose70-99%             -4.967e-01  2.129e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.32  on 936  degrees of freedom
AIC: 602.32

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' FTL ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: FTL 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.004049 
Standard error............: 0.053204 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.905 
Upper 95% CI..............: 1.114 
Z-value...................: 0.076094 
P-value...................: 0.939344 
Hosmer and Lemeshow r^2...: 0.084905 
Cox and Snell r^2.........: 0.053113 
Nagelkerke's pseudo r^2...: 0.112012 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of CD14.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.441e+01  1.957e+03   0.007  0.99413   
currentDF[, TARGET]       -6.326e-03  5.351e-02  -0.118  0.90590   
Age                        4.407e-02  1.511e-02   2.916  0.00354 **
Gendermale                -3.495e-01  2.780e-01  -1.257  0.20869   
ORdate_epoch               2.213e-04  8.952e-05   2.472  0.01345 * 
Hypertension.compositeyes -4.687e-01  4.193e-01  -1.118  0.26358   
DiabetesStatusDiabetes    -1.789e-01  2.628e-01  -0.681  0.49607   
SmokerStatusEx-smoker     -5.666e-01  2.760e-01  -2.053  0.04007 * 
SmokerStatusNever smoked  -8.970e-01  3.503e-01  -2.561  0.01045 * 
Med.Statin.LLDyes         -4.666e-03  2.960e-01  -0.016  0.98742   
Med.all.antiplateletyes   -7.528e-01  4.868e-01  -1.546  0.12200   
GFR_MDRD                   5.658e-03  6.095e-03   0.928  0.35326   
BMI                       -5.502e-02  2.901e-02  -1.897  0.05783 . 
MedHx_CVDyes              -1.056e-01  2.363e-01  -0.447  0.65492   
stenose50-70%             -1.350e+01  1.957e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.957e+03  -0.008  0.99375   
stenose90-99%             -1.534e+01  1.957e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.582e+01  1.957e+03  -0.008  0.99355   
stenose50-99%             -7.282e-01  2.777e+03   0.000  0.99979   
stenose70-99%             -4.982e-01  2.130e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.32  on 936  degrees of freedom
AIC: 602.32

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD14 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD14 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.006326 
Standard error............: 0.053513 
Odds ratio (effect size)..: 0.994 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.104 
Z-value...................: -0.118212 
P-value...................: 0.9058999 
Hosmer and Lemeshow r^2...: 0.084919 
Cox and Snell r^2.........: 0.053121 
Nagelkerke's pseudo r^2...: 0.112029 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of HCST.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.439e+01  1.957e+03   0.007  0.99413   
currentDF[, TARGET]       -2.353e-03  5.545e-02  -0.042  0.96615   
Age                        4.412e-02  1.510e-02   2.921  0.00349 **
Gendermale                -3.498e-01  2.781e-01  -1.258  0.20843   
ORdate_epoch               2.205e-04  8.927e-05   2.470  0.01352 * 
Hypertension.compositeyes -4.695e-01  4.193e-01  -1.120  0.26273   
DiabetesStatusDiabetes    -1.794e-01  2.627e-01  -0.683  0.49470   
SmokerStatusEx-smoker     -5.666e-01  2.760e-01  -2.053  0.04007 * 
SmokerStatusNever smoked  -8.984e-01  3.502e-01  -2.566  0.01030 * 
Med.Statin.LLDyes         -5.473e-03  2.959e-01  -0.018  0.98525   
Med.all.antiplateletyes   -7.531e-01  4.868e-01  -1.547  0.12185   
GFR_MDRD                   5.629e-03  6.090e-03   0.924  0.35533   
BMI                       -5.497e-02  2.900e-02  -1.896  0.05801 . 
MedHx_CVDyes              -1.049e-01  2.363e-01  -0.444  0.65717   
stenose50-70%             -1.349e+01  1.957e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.957e+03  -0.008  0.99375   
stenose90-99%             -1.533e+01  1.957e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.581e+01  1.957e+03  -0.008  0.99355   
stenose50-99%             -7.288e-01  2.776e+03   0.000  0.99979   
stenose70-99%             -4.994e-01  2.130e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.33  on 936  degrees of freedom
AIC: 602.33

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' HCST ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: HCST 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.002353 
Standard error............: 0.055446 
Odds ratio (effect size)..: 0.998 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.112 
Z-value...................: -0.04244 
P-value...................: 0.9661481 
Hosmer and Lemeshow r^2...: 0.084899 
Cox and Snell r^2.........: 0.053109 
Nagelkerke's pseudo r^2...: 0.112004 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of TIMP3.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.438e+01  1.958e+03   0.007  0.99414   
currentDF[, TARGET]       -8.633e-03  6.105e-02  -0.141  0.88755   
Age                        4.415e-02  1.510e-02   2.924  0.00346 **
Gendermale                -3.492e-01  2.780e-01  -1.256  0.20915   
ORdate_epoch               2.234e-04  9.170e-05   2.436  0.01487 * 
Hypertension.compositeyes -4.687e-01  4.192e-01  -1.118  0.26354   
DiabetesStatusDiabetes    -1.787e-01  2.628e-01  -0.680  0.49649   
SmokerStatusEx-smoker     -5.661e-01  2.760e-01  -2.051  0.04029 * 
SmokerStatusNever smoked  -8.984e-01  3.499e-01  -2.567  0.01025 * 
Med.Statin.LLDyes         -4.470e-03  2.959e-01  -0.015  0.98795   
Med.all.antiplateletyes   -7.526e-01  4.868e-01  -1.546  0.12209   
GFR_MDRD                   5.660e-03  6.093e-03   0.929  0.35289   
BMI                       -5.498e-02  2.900e-02  -1.896  0.05798 . 
MedHx_CVDyes              -1.050e-01  2.361e-01  -0.444  0.65670   
stenose50-70%             -1.349e+01  1.958e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.958e+03  -0.008  0.99376   
stenose90-99%             -1.533e+01  1.958e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.582e+01  1.958e+03  -0.008  0.99355   
stenose50-99%             -7.318e-01  2.777e+03   0.000  0.99979   
stenose70-99%             -4.978e-01  2.131e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.31  on 936  degrees of freedom
AIC: 602.31

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TIMP3 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TIMP3 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.008633 
Standard error............: 0.061047 
Odds ratio (effect size)..: 0.991 
Lower 95% CI..............: 0.88 
Upper 95% CI..............: 1.117 
Z-value...................: -0.141407 
P-value...................: 0.8875488 
Hosmer and Lemeshow r^2...: 0.084928 
Cox and Snell r^2.........: 0.053127 
Nagelkerke's pseudo r^2...: 0.112042 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of CCL2.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.418e+01  1.951e+03   0.007  0.99420   
currentDF[, TARGET]        3.808e-02  6.601e-02   0.577  0.56396   
Age                        4.450e-02  1.510e-02   2.946  0.00322 **
Gendermale                -3.493e-01  2.779e-01  -1.257  0.20882   
ORdate_epoch               2.137e-04  9.029e-05   2.367  0.01794 * 
Hypertension.compositeyes -4.772e-01  4.194e-01  -1.138  0.25525   
DiabetesStatusDiabetes    -1.842e-01  2.628e-01  -0.701  0.48344   
SmokerStatusEx-smoker     -5.657e-01  2.761e-01  -2.049  0.04047 * 
SmokerStatusNever smoked  -9.059e-01  3.502e-01  -2.587  0.00967 **
Med.Statin.LLDyes         -1.326e-02  2.959e-01  -0.045  0.96427   
Med.all.antiplateletyes   -7.619e-01  4.870e-01  -1.564  0.11775   
GFR_MDRD                   5.357e-03  6.072e-03   0.882  0.37767   
BMI                       -5.432e-02  2.900e-02  -1.873  0.06103 . 
MedHx_CVDyes              -9.723e-02  2.362e-01  -0.412  0.68065   
stenose50-70%             -1.344e+01  1.951e+03  -0.007  0.99450   
stenose70-90%             -1.529e+01  1.951e+03  -0.008  0.99375   
stenose90-99%             -1.529e+01  1.951e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.572e+01  1.951e+03  -0.008  0.99357   
stenose50-99%             -7.078e-01  2.773e+03   0.000  0.99980   
stenose70-99%             -4.964e-01  2.125e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 561.99  on 936  degrees of freedom
AIC: 601.99

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CCL2 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CCL2 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.038083 
Standard error............: 0.066005 
Odds ratio (effect size)..: 1.039 
Lower 95% CI..............: 0.913 
Upper 95% CI..............: 1.182 
Z-value...................: 0.576963 
P-value...................: 0.5639646 
Hosmer and Lemeshow r^2...: 0.08544 
Cox and Snell r^2.........: 0.053438 
Nagelkerke's pseudo r^2...: 0.112699 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of SAT1.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.432e+01  1.954e+03   0.007  0.99416   
currentDF[, TARGET]        9.649e-03  5.522e-02   0.175  0.86128   
Age                        4.421e-02  1.510e-02   2.928  0.00342 **
Gendermale                -3.515e-01  2.780e-01  -1.265  0.20604   
ORdate_epoch               2.186e-04  8.982e-05   2.434  0.01493 * 
Hypertension.compositeyes -4.725e-01  4.194e-01  -1.127  0.25992   
DiabetesStatusDiabetes    -1.798e-01  2.627e-01  -0.684  0.49371   
SmokerStatusEx-smoker     -5.663e-01  2.760e-01  -2.052  0.04019 * 
SmokerStatusNever smoked  -9.007e-01  3.501e-01  -2.573  0.01009 * 
Med.Statin.LLDyes         -7.887e-03  2.959e-01  -0.027  0.97874   
Med.all.antiplateletyes   -7.528e-01  4.867e-01  -1.547  0.12193   
GFR_MDRD                   5.541e-03  6.086e-03   0.911  0.36253   
BMI                       -5.490e-02  2.899e-02  -1.894  0.05825 . 
MedHx_CVDyes              -1.020e-01  2.364e-01  -0.432  0.66604   
stenose50-70%             -1.348e+01  1.954e+03  -0.007  0.99450   
stenose70-90%             -1.531e+01  1.954e+03  -0.008  0.99375   
stenose90-99%             -1.532e+01  1.954e+03  -0.008  0.99374   
stenose100% (Occlusion)   -1.578e+01  1.954e+03  -0.008  0.99356   
stenose50-99%             -7.248e-01  2.775e+03   0.000  0.99979   
stenose70-99%             -4.945e-01  2.128e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.5  on 955  degrees of freedom
Residual deviance: 562.3  on 936  degrees of freedom
AIC: 602.3

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' SAT1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: SAT1 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.009649 
Standard error............: 0.055215 
Odds ratio (effect size)..: 1.01 
Lower 95% CI..............: 0.906 
Upper 95% CI..............: 1.125 
Z-value...................: 0.174749 
P-value...................: 0.8612767 
Hosmer and Lemeshow r^2...: 0.084946 
Cox and Snell r^2.........: 0.053138 
Nagelkerke's pseudo r^2...: 0.112064 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of CD163.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.429e+01  1.954e+03   0.007  0.99417   
currentDF[, TARGET]        2.006e-02  8.229e-02   0.244  0.80745   
Age                        4.418e-02  1.509e-02   2.927  0.00342 **
Gendermale                -3.514e-01  2.779e-01  -1.265  0.20605   
ORdate_epoch               2.170e-04  9.035e-05   2.402  0.01633 * 
Hypertension.compositeyes -4.738e-01  4.195e-01  -1.130  0.25867   
DiabetesStatusDiabetes    -1.807e-01  2.628e-01  -0.688  0.49157   
SmokerStatusEx-smoker     -5.665e-01  2.760e-01  -2.053  0.04011 * 
SmokerStatusNever smoked  -9.021e-01  3.502e-01  -2.576  0.00998 **
Med.Statin.LLDyes         -7.550e-03  2.957e-01  -0.026  0.97963   
Med.all.antiplateletyes   -7.534e-01  4.867e-01  -1.548  0.12165   
GFR_MDRD                   5.521e-03  6.082e-03   0.908  0.36401   
BMI                       -5.489e-02  2.899e-02  -1.893  0.05831 . 
MedHx_CVDyes              -1.028e-01  2.361e-01  -0.435  0.66326   
stenose50-70%             -1.347e+01  1.954e+03  -0.007  0.99450   
stenose70-90%             -1.530e+01  1.954e+03  -0.008  0.99375   
stenose90-99%             -1.531e+01  1.954e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.577e+01  1.954e+03  -0.008  0.99356   
stenose50-99%             -7.158e-01  2.776e+03   0.000  0.99979   
stenose70-99%             -4.967e-01  2.128e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.27  on 936  degrees of freedom
AIC: 602.27

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' CD163 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: CD163 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.020057 
Standard error............: 0.082293 
Odds ratio (effect size)..: 1.02 
Lower 95% CI..............: 0.868 
Upper 95% CI..............: 1.199 
Z-value...................: 0.243721 
P-value...................: 0.807447 
Hosmer and Lemeshow r^2...: 0.084993 
Cox and Snell r^2.........: 0.053166 
Nagelkerke's pseudo r^2...: 0.112125 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of PTGDS.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.456e+01  1.962e+03   0.007  0.99408   
currentDF[, TARGET]       -4.915e-02  6.993e-02  -0.703  0.48220   
Age                        4.407e-02  1.510e-02   2.918  0.00352 **
Gendermale                -3.420e-01  2.783e-01  -1.229  0.21913   
ORdate_epoch               2.259e-04  8.942e-05   2.527  0.01151 * 
Hypertension.compositeyes -4.788e-01  4.196e-01  -1.141  0.25378   
DiabetesStatusDiabetes    -1.757e-01  2.630e-01  -0.668  0.50410   
SmokerStatusEx-smoker     -5.628e-01  2.764e-01  -2.036  0.04175 * 
SmokerStatusNever smoked  -8.915e-01  3.504e-01  -2.544  0.01095 * 
Med.Statin.LLDyes          7.546e-05  2.960e-01   0.000  0.99980   
Med.all.antiplateletyes   -7.504e-01  4.869e-01  -1.541  0.12323   
GFR_MDRD                   5.778e-03  6.111e-03   0.946  0.34440   
BMI                       -5.463e-02  2.903e-02  -1.882  0.05989 . 
MedHx_CVDyes              -1.032e-01  2.364e-01  -0.437  0.66240   
stenose50-70%             -1.354e+01  1.962e+03  -0.007  0.99449   
stenose70-90%             -1.537e+01  1.962e+03  -0.008  0.99375   
stenose90-99%             -1.539e+01  1.962e+03  -0.008  0.99374   
stenose100% (Occlusion)   -1.589e+01  1.962e+03  -0.008  0.99354   
stenose50-99%             -8.171e-01  2.779e+03   0.000  0.99977   
stenose70-99%             -5.183e-01  2.134e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 561.84  on 936  degrees of freedom
AIC: 601.84

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' PTGDS ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: PTGDS 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.049147 
Standard error............: 0.069934 
Odds ratio (effect size)..: 0.952 
Lower 95% CI..............: 0.83 
Upper 95% CI..............: 1.092 
Z-value...................: -0.702761 
P-value...................: 0.4822049 
Hosmer and Lemeshow r^2...: 0.08569 
Cox and Snell r^2.........: 0.05359 
Nagelkerke's pseudo r^2...: 0.113019 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of LGALS9.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.441e+01  1.957e+03   0.007   0.9941   
currentDF[, TARGET]       -5.889e-03  6.486e-02  -0.091   0.9276   
Age                        4.411e-02  1.510e-02   2.920   0.0035 **
Gendermale                -3.495e-01  2.780e-01  -1.257   0.2087   
ORdate_epoch               2.207e-04  8.930e-05   2.472   0.0134 * 
Hypertension.compositeyes -4.696e-01  4.192e-01  -1.120   0.2626   
DiabetesStatusDiabetes    -1.794e-01  2.628e-01  -0.683   0.4948   
SmokerStatusEx-smoker     -5.664e-01  2.760e-01  -2.052   0.0402 * 
SmokerStatusNever smoked  -8.975e-01  3.503e-01  -2.562   0.0104 * 
Med.Statin.LLDyes         -4.953e-03  2.959e-01  -0.017   0.9866   
Med.all.antiplateletyes   -7.537e-01  4.869e-01  -1.548   0.1216   
GFR_MDRD                   5.649e-03  6.094e-03   0.927   0.3539   
BMI                       -5.500e-02  2.900e-02  -1.896   0.0579 . 
MedHx_CVDyes              -1.054e-01  2.363e-01  -0.446   0.6557   
stenose50-70%             -1.349e+01  1.957e+03  -0.007   0.9945   
stenose70-90%             -1.532e+01  1.957e+03  -0.008   0.9938   
stenose90-99%             -1.533e+01  1.957e+03  -0.008   0.9937   
stenose100% (Occlusion)   -1.581e+01  1.957e+03  -0.008   0.9936   
stenose50-99%             -7.279e-01  2.776e+03   0.000   0.9998   
stenose70-99%             -4.997e-01  2.130e+03   0.000   0.9998   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.32  on 936  degrees of freedom
AIC: 602.32

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' LGALS9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: LGALS9 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.005889 
Standard error............: 0.064856 
Odds ratio (effect size)..: 0.994 
Lower 95% CI..............: 0.875 
Upper 95% CI..............: 1.129 
Z-value...................: -0.090809 
P-value...................: 0.9276445 
Hosmer and Lemeshow r^2...: 0.084909 
Cox and Snell r^2.........: 0.053115 
Nagelkerke's pseudo r^2...: 0.112017 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of ACKR1.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.420e+01  1.949e+03   0.007  0.99419   
currentDF[, TARGET]        4.774e-02  7.777e-02   0.614  0.53928   
Age                        4.441e-02  1.511e-02   2.939  0.00329 **
Gendermale                -3.628e-01  2.786e-01  -1.302  0.19276   
ORdate_epoch               2.169e-04  8.957e-05   2.421  0.01548 * 
Hypertension.compositeyes -4.715e-01  4.194e-01  -1.124  0.26091   
DiabetesStatusDiabetes    -1.805e-01  2.627e-01  -0.687  0.49209   
SmokerStatusEx-smoker     -5.697e-01  2.760e-01  -2.064  0.03902 * 
SmokerStatusNever smoked  -9.161e-01  3.512e-01  -2.608  0.00910 **
Med.Statin.LLDyes         -1.708e-02  2.962e-01  -0.058  0.95402   
Med.all.antiplateletyes   -7.554e-01  4.871e-01  -1.551  0.12094   
GFR_MDRD                   5.459e-03  6.069e-03   0.899  0.36839   
BMI                       -5.567e-02  2.902e-02  -1.918  0.05506 . 
MedHx_CVDyes              -9.763e-02  2.362e-01  -0.413  0.67935   
stenose50-70%             -1.344e+01  1.949e+03  -0.007  0.99450   
stenose70-90%             -1.528e+01  1.949e+03  -0.008  0.99374   
stenose90-99%             -1.529e+01  1.949e+03  -0.008  0.99374   
stenose100% (Occlusion)   -1.574e+01  1.949e+03  -0.008  0.99356   
stenose50-99%             -6.800e-01  2.772e+03   0.000  0.99980   
stenose70-99%             -4.803e-01  2.123e+03   0.000  0.99982   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 561.95  on 936  degrees of freedom
AIC: 601.95

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' ACKR1 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: ACKR1 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.047743 
Standard error............: 0.07777 
Odds ratio (effect size)..: 1.049 
Lower 95% CI..............: 0.901 
Upper 95% CI..............: 1.222 
Z-value...................: 0.613902 
P-value...................: 0.5392799 
Hosmer and Lemeshow r^2...: 0.085518 
Cox and Snell r^2.........: 0.053486 
Nagelkerke's pseudo r^2...: 0.112799 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of NT5DC2.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.436e+01  1.956e+03   0.007  0.99414   
currentDF[, TARGET]        4.412e-03  7.202e-02   0.061  0.95115   
Age                        4.414e-02  1.510e-02   2.924  0.00346 **
Gendermale                -3.502e-01  2.779e-01  -1.260  0.20765   
ORdate_epoch               2.192e-04  9.085e-05   2.413  0.01581 * 
Hypertension.compositeyes -4.702e-01  4.192e-01  -1.122  0.26193   
DiabetesStatusDiabetes    -1.796e-01  2.628e-01  -0.684  0.49425   
SmokerStatusEx-smoker     -5.665e-01  2.760e-01  -2.052  0.04012 * 
SmokerStatusNever smoked  -8.990e-01  3.499e-01  -2.569  0.01019 * 
Med.Statin.LLDyes         -6.464e-03  2.959e-01  -0.022  0.98257   
Med.all.antiplateletyes   -7.531e-01  4.867e-01  -1.547  0.12183   
GFR_MDRD                   5.589e-03  6.090e-03   0.918  0.35876   
BMI                       -5.499e-02  2.900e-02  -1.896  0.05794 . 
MedHx_CVDyes              -1.039e-01  2.362e-01  -0.440  0.66017   
stenose50-70%             -1.349e+01  1.956e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.956e+03  -0.008  0.99375   
stenose90-99%             -1.533e+01  1.956e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.579e+01  1.956e+03  -0.008  0.99356   
stenose50-99%             -7.256e-01  2.776e+03   0.000  0.99979   
stenose70-99%             -4.991e-01  2.129e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.33  on 936  degrees of freedom
AIC: 602.33

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' NT5DC2 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: NT5DC2 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.004412 
Standard error............: 0.072022 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.872 
Upper 95% CI..............: 1.157 
Z-value...................: 0.06126 
P-value...................: 0.9511522 
Hosmer and Lemeshow r^2...: 0.084902 
Cox and Snell r^2.........: 0.053111 
Nagelkerke's pseudo r^2...: 0.112008 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of TGFBI.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.435e+01  1.955e+03   0.007  0.99414   
currentDF[, TARGET]        8.955e-03  6.554e-02   0.137  0.89132   
Age                        4.418e-02  1.510e-02   2.926  0.00343 **
Gendermale                -3.496e-01  2.779e-01  -1.258  0.20848   
ORdate_epoch               2.175e-04  9.164e-05   2.373  0.01765 * 
Hypertension.compositeyes -4.714e-01  4.193e-01  -1.124  0.26092   
DiabetesStatusDiabetes    -1.798e-01  2.628e-01  -0.684  0.49369   
SmokerStatusEx-smoker     -5.665e-01  2.760e-01  -2.053  0.04010 * 
SmokerStatusNever smoked  -8.994e-01  3.499e-01  -2.570  0.01016 * 
Med.Statin.LLDyes         -6.352e-03  2.957e-01  -0.021  0.98286   
Med.all.antiplateletyes   -7.533e-01  4.867e-01  -1.548  0.12170   
GFR_MDRD                   5.559e-03  6.086e-03   0.913  0.36106   
BMI                       -5.491e-02  2.899e-02  -1.894  0.05826 . 
MedHx_CVDyes              -1.029e-01  2.363e-01  -0.436  0.66316   
stenose50-70%             -1.348e+01  1.955e+03  -0.007  0.99450   
stenose70-90%             -1.531e+01  1.955e+03  -0.008  0.99375   
stenose90-99%             -1.532e+01  1.955e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.578e+01  1.955e+03  -0.008  0.99356   
stenose50-99%             -7.231e-01  2.775e+03   0.000  0.99979   
stenose70-99%             -4.975e-01  2.128e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.31  on 936  degrees of freedom
AIC: 602.31

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' TGFBI ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: TGFBI 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.008955 
Standard error............: 0.065543 
Odds ratio (effect size)..: 1.009 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.147 
Z-value...................: 0.136629 
P-value...................: 0.8913237 
Hosmer and Lemeshow r^2...: 0.084926 
Cox and Snell r^2.........: 0.053126 
Nagelkerke's pseudo r^2...: 0.112039 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of C1QC.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.439e+01  1.957e+03   0.007  0.99413   
currentDF[, TARGET]       -2.768e-03  6.013e-02  -0.046  0.96328   
Age                        4.412e-02  1.510e-02   2.922  0.00348 **
Gendermale                -3.497e-01  2.781e-01  -1.257  0.20858   
ORdate_epoch               2.204e-04  8.923e-05   2.470  0.01349 * 
Hypertension.compositeyes -4.695e-01  4.193e-01  -1.120  0.26278   
DiabetesStatusDiabetes    -1.794e-01  2.627e-01  -0.683  0.49467   
SmokerStatusEx-smoker     -5.665e-01  2.760e-01  -2.052  0.04012 * 
SmokerStatusNever smoked  -8.984e-01  3.502e-01  -2.565  0.01031 * 
Med.Statin.LLDyes         -5.539e-03  2.959e-01  -0.019  0.98506   
Med.all.antiplateletyes   -7.530e-01  4.868e-01  -1.547  0.12188   
GFR_MDRD                   5.628e-03  6.088e-03   0.924  0.35523   
BMI                       -5.499e-02  2.901e-02  -1.896  0.05800 . 
MedHx_CVDyes              -1.049e-01  2.363e-01  -0.444  0.65701   
stenose50-70%             -1.349e+01  1.957e+03  -0.007  0.99450   
stenose70-90%             -1.532e+01  1.957e+03  -0.008  0.99375   
stenose90-99%             -1.533e+01  1.957e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.581e+01  1.957e+03  -0.008  0.99355   
stenose50-99%             -7.285e-01  2.776e+03   0.000  0.99979   
stenose70-99%             -4.983e-01  2.130e+03   0.000  0.99981   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 562.33  on 936  degrees of freedom
AIC: 602.33

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' C1QC ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: C1QC 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.002768 
Standard error............: 0.060132 
Odds ratio (effect size)..: 0.997 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.122 
Z-value...................: -0.046036 
P-value...................: 0.9632813 
Hosmer and Lemeshow r^2...: 0.084899 
Cox and Snell r^2.........: 0.053109 
Nagelkerke's pseudo r^2...: 0.112005 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421 

Analysis of S100A9.

- processing AsymptSympt

filter: removed 136 rows (12%), 956 rows remainingfilter_if: no rows removed


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_epoch + 
    SmokerStatus + Med.all.antiplatelet + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
             (Intercept)                       Age              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -1.7358395                 0.0395793                 0.0002806                -0.6426984                -0.8869290  
 Med.all.antiplateletyes                       BMI  
              -0.6869249                -0.0582899  

Degrees of Freedom: 955 Total (i.e. Null);  949 Residual
Null Deviance:      614.5 
Residual Deviance: 577.3    AIC: 591.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, TARGET] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.450e+01  1.962e+03   0.007  0.99410   
currentDF[, TARGET]       -4.869e-02  7.748e-02  -0.628  0.52979   
Age                        4.425e-02  1.512e-02   2.927  0.00342 **
Gendermale                -3.442e-01  2.783e-01  -1.237  0.21611   
ORdate_epoch               2.276e-04  8.964e-05   2.539  0.01112 * 
Hypertension.compositeyes -4.652e-01  4.194e-01  -1.109  0.26726   
DiabetesStatusDiabetes    -1.831e-01  2.629e-01  -0.696  0.48615   
SmokerStatusEx-smoker     -5.702e-01  2.762e-01  -2.064  0.03901 * 
SmokerStatusNever smoked  -8.961e-01  3.501e-01  -2.560  0.01048 * 
Med.Statin.LLDyes          4.087e-03  2.963e-01   0.014  0.98899   
Med.all.antiplateletyes   -7.502e-01  4.871e-01  -1.540  0.12351   
GFR_MDRD                   5.847e-03  6.111e-03   0.957  0.33869   
BMI                       -5.502e-02  2.903e-02  -1.895  0.05805 . 
MedHx_CVDyes              -1.076e-01  2.364e-01  -0.455  0.64894   
stenose50-70%             -1.353e+01  1.962e+03  -0.007  0.99450   
stenose70-90%             -1.536e+01  1.962e+03  -0.008  0.99375   
stenose90-99%             -1.538e+01  1.962e+03  -0.008  0.99375   
stenose100% (Occlusion)   -1.587e+01  1.962e+03  -0.008  0.99355   
stenose50-99%             -7.559e-01  2.777e+03   0.000  0.99978   
stenose70-99%             -4.933e-01  2.135e+03   0.000  0.99982   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.50  on 955  degrees of freedom
Residual deviance: 561.94  on 936  degrees of freedom
AIC: 601.94

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AERNASE.clin.targets ' the association of ' S100A9 ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AERNASE.clin.targets 
Score/Exposure/biomarker..: S100A9 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.048686 
Standard error............: 0.077485 
Odds ratio (effect size)..: 0.952 
Lower 95% CI..............: 0.818 
Upper 95% CI..............: 1.109 
Z-value...................: -0.628327 
P-value...................: 0.5297899 
Hosmer and Lemeshow r^2...: 0.085534 
Cox and Snell r^2.........: 0.053495 
Nagelkerke's pseudo r^2...: 0.112818 
Sample size of AE DB......: 1092 
Sample size of model......: 956 
Missing data %............: 12.45421

cat("Edit the column names...\n")

Edit the column names...

colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")

Correct the variable types...

GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

Writing results to Excel-file...

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"),
           rowNames = FALSE, colNmes = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")

Removing intermediate files...

rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque CONVOCALS_downstream expression levels and secondary cardiovascular events over a three-year follow-up period.

The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..

The secondary outcomes will be

incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: epstroke.3years, these include:
- cerebral infarction (CVA/stroke)
- cerebral bleeding (CVA/stroke)
- fatal cerebral infarction
- fatal cerebral bleeding.
incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: epcoronary.3years, these include:
- myocardial infarction (MI)
- coronary angioplasty (PCI/PTCA)
- cardiovascular death (exact cause to be investigated)
- coronary bypass (CABG)
- fatal myocardial infarction (MI)
- sudden death.
cardiovascular death - variable: epcvdeath.3years, these include:
- cardiovascular death (exact cause to be investigated)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death.

AERNASE.clin.targets <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "max.followup", 
                                                        "FU.cutt.off.3years", 
                                                        "ep_major_t_3years"
                                                        # , "ep_stroke_t_3years", "ep_coronary_t_3years",
                                                        # "ep_cvdeath_t_3years",
                                                        # "epstroke.3years", "epcoronary.3years", "epcvdeath.3years"
                                                        )), 
                            by.x = "study_number", by.y = "STUDY_NUMBER", sort = TRUE)

AERNASE.clin.targets.temp <- subset(AERNASE.clin.targets,  select = c("study_number", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years"))
require(labelled)
AERNASE.clin.targets.temp$Gender <- to_factor(AERNASE.clin.targets.temp$Gender)
AERNASE.clin.targets.temp$Hospital <- to_factor(AERNASE.clin.targets.temp$Hospital)
AERNASE.clin.targets.temp$Artery_summary <- to_factor(AERNASE.clin.targets.temp$Artery_summary)

DT::datatable(AERNASE.clin.targets.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.targets.", rownames = FALSE)


rm(AERNASE.clin.targets.temp)

Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})

cat("* Creating function to summarize Cox regression and prepare container for results.")

* Creating function to summarize Cox regression and prepare container for results.

# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, target_of_interest){
  cat("Summarizing Cox regression results for '", target_of_interest ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(target_of_interest, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, target_of_interest, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", target_of_interest, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

# times = c("ep_major_t_3years", 
#           "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")
# 
# endpoints = c("epmajor.3years", 
#               "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

times = c("ep_major_t_3years")

endpoints = c("epmajor.3years")

cat("* Check the cases per event type - for sanity.")

* Check the cases per event type - for sanity.

for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.targets[,events]))
  print(table(AERNASE.clin.targets[,events]))
}

[1] "Printing the summary of: epmajor.3years"

  0   1 
950 131

cat("* Check distribution of events over time - for sanity.")

* Check distribution of events over time - for sanity.

for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.targets[,eventtimes]))
}

[1] "Printing the summary of: ep_major_t_3years"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   2.912   3.000   2.640   3.000   3.000      15

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.targets, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.targets.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

[1] "Printing the distribution of: ep_major_t_3years"

Cox regressions

Let’s perform the actual Cox-regressions. We will apply a couple of models:

Model 1: adjusted for age, sex, and year of surgery
Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

3 years follow-up

Model 1

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.targets)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.targets.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.targets.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.targets.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.targets", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [SCGB3A2]; 1 out of 35 target-of-interest.
   > cross tabulation of SCGB3A2-stratum.

[2.37, 5.44) [5.44,10.32] 
         546          546 

   > fitting the model for SCGB3A2-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.44,10.32] -0.24755   0.78071  0.18196 -1.360  0.17369    
Age                                                            0.03814   1.03887  0.01069  3.568  0.00036 ***
Gendermale                                                     0.49994   1.64862  0.21772  2.296  0.02166 *  
ORdate_year2002                                                0.98801   2.68587  1.07420  0.920  0.35770    
ORdate_year2003                                               -0.02423   0.97606  1.09939 -0.022  0.98241    
ORdate_year2004                                                0.23676   1.26714  1.06548  0.222  0.82415    
ORdate_year2005                                                0.55808   1.74732  1.04201  0.536  0.59225    
ORdate_year2006                                                0.61005   1.84052  1.03674  0.588  0.55625    
ORdate_year2007                                               -0.32242   0.72440  1.08166 -0.298  0.76565    
ORdate_year2008                                                0.70969   2.03336  1.03456  0.686  0.49272    
ORdate_year2009                                                0.11693   1.12404  1.05338  0.111  0.91161    
ORdate_year2010                                                0.26388   1.30197  1.06165  0.249  0.80371    
ORdate_year2011                                                0.36211   1.43636  1.07117  0.338  0.73532    
ORdate_year2012                                                0.95702   2.60392  1.03877  0.921  0.35689    
ORdate_year2013                                                0.84983   2.33926  1.04173  0.816  0.41462    
ORdate_year2014                                                0.32310   1.38140  1.06971  0.302  0.76262    
ORdate_year2015                                               -0.12064   0.88635  1.22516 -0.098  0.92156    
ORdate_year2016                                               -0.09915   0.90561  1.22509 -0.081  0.93549    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.44,10.32]    0.7807     1.2809   0.54651     1.115
Age                                                              1.0389     0.9626   1.01733     1.061
Gendermale                                                       1.6486     0.6066   1.07597     2.526
ORdate_year2002                                                  2.6859     0.3723   0.32713    22.052
ORdate_year2003                                                  0.9761     1.0245   0.11315     8.419
ORdate_year2004                                                  1.2671     0.7892   0.15699    10.227
ORdate_year2005                                                  1.7473     0.5723   0.22668    13.469
ORdate_year2006                                                  1.8405     0.5433   0.24125    14.042
ORdate_year2007                                                  0.7244     1.3805   0.08695     6.035
ORdate_year2008                                                  2.0334     0.4918   0.26767    15.446
ORdate_year2009                                                  1.1240     0.8896   0.14261     8.860
ORdate_year2010                                                  1.3020     0.7681   0.16253    10.430
ORdate_year2011                                                  1.4364     0.6962   0.17599    11.723
ORdate_year2012                                                  2.6039     0.3840   0.33996    19.945
ORdate_year2013                                                  2.3393     0.4275   0.30364    18.022
ORdate_year2014                                                  1.3814     0.7239   0.16974    11.243
ORdate_year2015                                                  0.8863     1.1282   0.08031     9.783
ORdate_year2016                                                  0.9056     1.1042   0.08206     9.994
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.643  (se = 0.023 )
Likelihood ratio test= 36.53  on 18 df,   p=0.006
Wald test            = 34.72  on 18 df,   p=0.01
Score (logrank) test = 35.58  on 18 df,   p=0.008


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' SCGB3A2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: SCGB3A2 
Effect size...............: -0.247552 
Standard error............: 0.181965 
Odds ratio (effect size)..: 0.781 
Lower 95% CI..............: 0.547 
Upper 95% CI..............: 1.115 
T-value...................: -1.360439 
P-value...................: 0.1736911 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [LIX1]; 2 out of 35 target-of-interest.
   > cross tabulation of LIX1-stratum.

[5.45, 7.95) [7.95,10.73] 
         546          546 

   > fitting the model for LIX1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.95,10.73]  0.19475   1.21501  0.18178  1.071 0.284030    
Age                                                            0.03846   1.03921  0.01071  3.589 0.000331 ***
Gendermale                                                     0.51811   1.67886  0.21711  2.386 0.017016 *  
ORdate_year2002                                                1.02453   2.78579  1.07298  0.955 0.339655    
ORdate_year2003                                               -0.02042   0.97978  1.10058 -0.019 0.985195    
ORdate_year2004                                                0.29577   1.34416  1.06340  0.278 0.780909    
ORdate_year2005                                                0.60259   1.82684  1.04069  0.579 0.562569    
ORdate_year2006                                                0.64978   1.91511  1.03559  0.627 0.530369    
ORdate_year2007                                               -0.33036   0.71867  1.08286 -0.305 0.760306    
ORdate_year2008                                                0.73799   2.09173  1.03394  0.714 0.475369    
ORdate_year2009                                                0.21395   1.23856  1.04952  0.204 0.838464    
ORdate_year2010                                                0.26440   1.30265  1.06207  0.249 0.803398    
ORdate_year2011                                                0.39049   1.47770  1.07060  0.365 0.715309    
ORdate_year2012                                                0.98104   2.66724  1.03838  0.945 0.344770    
ORdate_year2013                                                0.86976   2.38633  1.04140  0.835 0.403616    
ORdate_year2014                                                0.37231   1.45108  1.06940  0.348 0.727728    
ORdate_year2015                                               -0.07865   0.92436  1.22507 -0.064 0.948810    
ORdate_year2016                                               -0.08597   0.91762  1.22507 -0.070 0.944056    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.95,10.73]    1.2150     0.8230   0.85083     1.735
Age                                                              1.0392     0.9623   1.01761     1.061
Gendermale                                                       1.6789     0.5956   1.09700     2.569
ORdate_year2002                                                  2.7858     0.3590   0.34012    22.818
ORdate_year2003                                                  0.9798     1.0206   0.11332     8.471
ORdate_year2004                                                  1.3442     0.7440   0.16722    10.805
ORdate_year2005                                                  1.8268     0.5474   0.23761    14.046
ORdate_year2006                                                  1.9151     0.5222   0.25159    14.578
ORdate_year2007                                                  0.7187     1.3915   0.08606     6.001
ORdate_year2008                                                  2.0917     0.4781   0.27569    15.871
ORdate_year2009                                                  1.2386     0.8074   0.15833     9.689
ORdate_year2010                                                  1.3027     0.7677   0.16248    10.444
ORdate_year2011                                                  1.4777     0.6767   0.18125    12.047
ORdate_year2012                                                  2.6672     0.3749   0.34849    20.414
ORdate_year2013                                                  2.3863     0.4191   0.30995    18.373
ORdate_year2014                                                  1.4511     0.6891   0.17841    11.802
ORdate_year2015                                                  0.9244     1.0818   0.08376    10.201
ORdate_year2016                                                  0.9176     1.0898   0.08315    10.126
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.64  (se = 0.023 )
Likelihood ratio test= 35.82  on 18 df,   p=0.007
Wald test            = 34.08  on 18 df,   p=0.01
Score (logrank) test = 34.9  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LIX1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: LIX1 
Effect size...............: 0.194748 
Standard error............: 0.181785 
Odds ratio (effect size)..: 1.215 
Lower 95% CI..............: 0.851 
Upper 95% CI..............: 1.735 
T-value...................: 1.071311 
P-value...................: 0.2840297 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [IGSF9B]; 3 out of 35 target-of-interest.
   > cross tabulation of IGSF9B-stratum.

[2.37, 5.88) [5.88,11.00] 
         546          546 

   > fitting the model for IGSF9B-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.88,11.00] -0.38503   0.68043  0.18742 -2.054 0.039941 *  
Age                                                            0.03832   1.03907  0.01074  3.569 0.000359 ***
Gendermale                                                     0.47252   1.60403  0.21836  2.164 0.030467 *  
ORdate_year2002                                                0.90614   2.47476  1.07523  0.843 0.399372    
ORdate_year2003                                               -0.13105   0.87717  1.10138 -0.119 0.905284    
ORdate_year2004                                                0.16025   1.17380  1.06619  0.150 0.880528    
ORdate_year2005                                                0.50229   1.65251  1.04207  0.482 0.629793    
ORdate_year2006                                                0.58170   1.78908  1.03586  0.562 0.574412    
ORdate_year2007                                               -0.33623   0.71446  1.08126 -0.311 0.755832    
ORdate_year2008                                                0.65458   1.92434  1.03516  0.632 0.527157    
ORdate_year2009                                                0.05410   1.05559  1.05321  0.051 0.959035    
ORdate_year2010                                                0.22229   1.24893  1.06200  0.209 0.834204    
ORdate_year2011                                                0.35874   1.43152  1.07013  0.335 0.737452    
ORdate_year2012                                                0.91422   2.49484  1.03909  0.880 0.378953    
ORdate_year2013                                                0.85577   2.35319  1.04132  0.822 0.411181    
ORdate_year2014                                                0.34337   1.40969  1.06942  0.321 0.748151    
ORdate_year2015                                               -0.10633   0.89913  1.22494 -0.087 0.930829    
ORdate_year2016                                               -0.05966   0.94209  1.22485 -0.049 0.961154    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.88,11.00]    0.6804     1.4697   0.47125    0.9825
Age                                                              1.0391     0.9624   1.01743    1.0612
Gendermale                                                       1.6040     0.6234   1.04555    2.4608
ORdate_year2002                                                  2.4748     0.4041   0.30081   20.3597
ORdate_year2003                                                  0.8772     1.1400   0.10130    7.5959
ORdate_year2004                                                  1.1738     0.8519   0.14523    9.4871
ORdate_year2005                                                  1.6525     0.6051   0.21436   12.7395
ORdate_year2006                                                  1.7891     0.5589   0.23491   13.6255
ORdate_year2007                                                  0.7145     1.3997   0.08582    5.9477
ORdate_year2008                                                  1.9243     0.5197   0.25302   14.6356
ORdate_year2009                                                  1.0556     0.9473   0.13397    8.3174
ORdate_year2010                                                  1.2489     0.8007   0.15580   10.0119
ORdate_year2011                                                  1.4315     0.6986   0.17575   11.6598
ORdate_year2012                                                  2.4948     0.4008   0.32551   19.1215
ORdate_year2013                                                  2.3532     0.4250   0.30569   18.1145
ORdate_year2014                                                  1.4097     0.7094   0.17331   11.4662
ORdate_year2015                                                  0.8991     1.1122   0.08150    9.9197
ORdate_year2016                                                  0.9421     1.0615   0.08541   10.3916
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.652  (se = 0.023 )
Likelihood ratio test= 38.96  on 18 df,   p=0.003
Wald test            = 37.22  on 18 df,   p=0.005
Score (logrank) test = 38.19  on 18 df,   p=0.004


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IGSF9B ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: IGSF9B 
Effect size...............: -0.385034 
Standard error............: 0.187423 
Odds ratio (effect size)..: 0.68 
Lower 95% CI..............: 0.471 
Upper 95% CI..............: 0.982 
T-value...................: -2.054363 
P-value...................: 0.03994055 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [ALB]; 4 out of 35 target-of-interest.
   > cross tabulation of ALB-stratum.

[2.37,6.18) [6.18,9.89] 
        546         546 

   > fitting the model for ALB-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                 coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.18,9.89] -0.16829   0.84511  0.17910 -0.940 0.347391    
Age                                                           0.03734   1.03805  0.01069  3.492 0.000479 ***
Gendermale                                                    0.50636   1.65925  0.21750  2.328 0.019907 *  
ORdate_year2002                                               1.13791   3.12023  1.07170  1.062 0.288339    
ORdate_year2003                                               0.10885   1.11500  1.09728  0.099 0.920976    
ORdate_year2004                                               0.36682   1.44314  1.06202  0.345 0.729794    
ORdate_year2005                                               0.67094   1.95607  1.03860  0.646 0.518279    
ORdate_year2006                                               0.72120   2.05691  1.03347  0.698 0.485271    
ORdate_year2007                                              -0.23260   0.79247  1.08062 -0.215 0.829572    
ORdate_year2008                                               0.79648   2.21771  1.03312  0.771 0.440739    
ORdate_year2009                                               0.22966   1.25817  1.04915  0.219 0.826731    
ORdate_year2010                                               0.33417   1.39678  1.06094  0.315 0.752784    
ORdate_year2011                                               0.46735   1.59577  1.06926  0.437 0.662053    
ORdate_year2012                                               1.03792   2.82335  1.03815  1.000 0.317417    
ORdate_year2013                                               0.89159   2.43900  1.04099  0.856 0.391734    
ORdate_year2014                                               0.33890   1.40341  1.06950  0.317 0.751334    
ORdate_year2015                                              -0.06772   0.93452  1.22530 -0.055 0.955924    
ORdate_year2016                                              -0.05016   0.95108  1.22504 -0.041 0.967338    
ORdate_year2017                                                    NA        NA  0.00000     NA       NA    
ORdate_year2018                                                    NA        NA  0.00000     NA       NA    
ORdate_year2019                                                    NA        NA  0.00000     NA       NA    
ORdate_year2020                                                    NA        NA  0.00000     NA       NA    
ORdate_year2021                                                    NA        NA  0.00000     NA       NA    
ORdate_year2022                                                    NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.18,9.89]    0.8451     1.1833   0.59493     1.200
Age                                                             1.0380     0.9633   1.01652     1.060
Gendermale                                                      1.6592     0.6027   1.08336     2.541
ORdate_year2002                                                 3.1202     0.3205   0.38190    25.493
ORdate_year2003                                                 1.1150     0.8969   0.12980     9.578
ORdate_year2004                                                 1.4431     0.6929   0.18002    11.569
ORdate_year2005                                                 1.9561     0.5112   0.25546    14.978
ORdate_year2006                                                 2.0569     0.4862   0.27135    15.592
ORdate_year2007                                                 0.7925     1.2619   0.09531     6.589
ORdate_year2008                                                 2.2177     0.4509   0.29276    16.799
ORdate_year2009                                                 1.2582     0.7948   0.16095     9.835
ORdate_year2010                                                 1.3968     0.7159   0.17460    11.174
ORdate_year2011                                                 1.5958     0.6267   0.19625    12.976
ORdate_year2012                                                 2.8233     0.3542   0.36905    21.599
ORdate_year2013                                                 2.4390     0.4100   0.31704    18.763
ORdate_year2014                                                 1.4034     0.7126   0.17251    11.417
ORdate_year2015                                                 0.9345     1.0701   0.08465    10.317
ORdate_year2016                                                 0.9511     1.0514   0.08619    10.495
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.639  (se = 0.024 )
Likelihood ratio test= 35.56  on 18 df,   p=0.008
Wald test            = 33.94  on 18 df,   p=0.01
Score (logrank) test = 34.81  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' ALB ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: ALB 
Effect size...............: -0.16829 
Standard error............: 0.179096 
Odds ratio (effect size)..: 0.845 
Lower 95% CI..............: 0.595 
Upper 95% CI..............: 1.201 
T-value...................: -0.939663 
P-value...................: 0.3473906 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [OR10A4]; 5 out of 35 target-of-interest.
   > cross tabulation of OR10A4-stratum.

[2.37,5.66) [5.66,9.58] 
        546         546 

   > fitting the model for OR10A4-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                 coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.66,9.58] -0.25139   0.77772  0.18067 -1.391 0.164107    
Age                                                           0.03659   1.03726  0.01071  3.415 0.000638 ***
Gendermale                                                    0.49922   1.64743  0.21756  2.295 0.021756 *  
ORdate_year2002                                               1.15772   3.18266  1.07159  1.080 0.279977    
ORdate_year2003                                               0.13287   1.14210  1.09745  0.121 0.903637    
ORdate_year2004                                               0.36089   1.43461  1.06183  0.340 0.733948    
ORdate_year2005                                               0.67276   1.95964  1.03851  0.648 0.517108    
ORdate_year2006                                               0.71506   2.04431  1.03339  0.692 0.488963    
ORdate_year2007                                              -0.23100   0.79374  1.08052 -0.214 0.830711    
ORdate_year2008                                               0.80537   2.23753  1.03310  0.780 0.435644    
ORdate_year2009                                               0.23310   1.26251  1.04902  0.222 0.824152    
ORdate_year2010                                               0.34236   1.40827  1.06097  0.323 0.746935    
ORdate_year2011                                               0.49544   1.64123  1.06963  0.463 0.643226    
ORdate_year2012                                               1.04288   2.83737  1.03804  1.005 0.315060    
ORdate_year2013                                               0.88421   2.42106  1.04099  0.849 0.395663    
ORdate_year2014                                               0.32579   1.38513  1.06950  0.305 0.760654    
ORdate_year2015                                              -0.09350   0.91074  1.22504 -0.076 0.939163    
ORdate_year2016                                              -0.06060   0.94120  1.22492 -0.049 0.960542    
ORdate_year2017                                                    NA        NA  0.00000     NA       NA    
ORdate_year2018                                                    NA        NA  0.00000     NA       NA    
ORdate_year2019                                                    NA        NA  0.00000     NA       NA    
ORdate_year2020                                                    NA        NA  0.00000     NA       NA    
ORdate_year2021                                                    NA        NA  0.00000     NA       NA    
ORdate_year2022                                                    NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.66,9.58]    0.7777     1.2858   0.54580     1.108
Age                                                             1.0373     0.9641   1.01571     1.059
Gendermale                                                      1.6474     0.6070   1.07552     2.523
ORdate_year2002                                                 3.1827     0.3142   0.38963    25.997
ORdate_year2003                                                 1.1421     0.8756   0.13291     9.814
ORdate_year2004                                                 1.4346     0.6971   0.17902    11.496
ORdate_year2005                                                 1.9596     0.5103   0.25597    15.002
ORdate_year2006                                                 2.0443     0.4892   0.26973    15.494
ORdate_year2007                                                 0.7937     1.2599   0.09549     6.598
ORdate_year2008                                                 2.2375     0.4469   0.29539    16.949
ORdate_year2009                                                 1.2625     0.7921   0.16155     9.866
ORdate_year2010                                                 1.4083     0.7101   0.17603    11.266
ORdate_year2011                                                 1.6412     0.6093   0.20170    13.355
ORdate_year2012                                                 2.8374     0.3524   0.37097    21.702
ORdate_year2013                                                 2.4211     0.4130   0.31471    18.625
ORdate_year2014                                                 1.3851     0.7220   0.17027    11.268
ORdate_year2015                                                 0.9107     1.0980   0.08253    10.050
ORdate_year2016                                                 0.9412     1.0625   0.08532    10.383
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.642  (se = 0.024 )
Likelihood ratio test= 36.62  on 18 df,   p=0.006
Wald test            = 35.03  on 18 df,   p=0.009
Score (logrank) test = 35.97  on 18 df,   p=0.007


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' OR10A4 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: OR10A4 
Effect size...............: -0.251389 
Standard error............: 0.180675 
Odds ratio (effect size)..: 0.778 
Lower 95% CI..............: 0.546 
Upper 95% CI..............: 1.108 
T-value...................: -1.391392 
P-value...................: 0.1641067 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [RASEF]; 6 out of 35 target-of-interest.
   > cross tabulation of RASEF-stratum.

[2.37, 7.51) [7.51,10.20] 
         546          546 

   > fitting the model for RASEF-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.51,10.20] -0.10192   0.90310  0.17934 -0.568 0.569821    
Age                                                            0.03811   1.03885  0.01070  3.562 0.000368 ***
Gendermale                                                     0.50683   1.66002  0.21799  2.325 0.020072 *  
ORdate_year2002                                                1.09469   2.98826  1.07109  1.022 0.306765    
ORdate_year2003                                                0.07166   1.07429  1.09688  0.065 0.947913    
ORdate_year2004                                                0.33574   1.39897  1.06237  0.316 0.751982    
ORdate_year2005                                                0.65103   1.91751  1.03923  0.626 0.531019    
ORdate_year2006                                                0.70735   2.02860  1.03375  0.684 0.493815    
ORdate_year2007                                               -0.25365   0.77597  1.08038 -0.235 0.814384    
ORdate_year2008                                                0.78144   2.18463  1.03310  0.756 0.449405    
ORdate_year2009                                                0.22730   1.25521  1.04958  0.217 0.828546    
ORdate_year2010                                                0.32668   1.38636  1.06089  0.308 0.758132    
ORdate_year2011                                                0.45015   1.56855  1.06910  0.421 0.673717    
ORdate_year2012                                                1.02172   2.77798  1.03793  0.984 0.324924    
ORdate_year2013                                                0.89031   2.43587  1.04115  0.855 0.392487    
ORdate_year2014                                                0.35251   1.42263  1.06944  0.330 0.741685    
ORdate_year2015                                               -0.07703   0.92586  1.22523 -0.063 0.949871    
ORdate_year2016                                               -0.03792   0.96279  1.22603 -0.031 0.975326    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.51,10.20]    0.9031     1.1073   0.63546     1.283
Age                                                              1.0388     0.9626   1.01729     1.061
Gendermale                                                       1.6600     0.6024   1.08283     2.545
ORdate_year2002                                                  2.9883     0.3346   0.36619    24.386
ORdate_year2003                                                  1.0743     0.9309   0.12516     9.221
ORdate_year2004                                                  1.3990     0.7148   0.17439    11.223
ORdate_year2005                                                  1.9175     0.5215   0.25012    14.700
ORdate_year2006                                                  2.0286     0.4930   0.26747    15.386
ORdate_year2007                                                  0.7760     1.2887   0.09337     6.449
ORdate_year2008                                                  2.1846     0.4577   0.28840    16.548
ORdate_year2009                                                  1.2552     0.7967   0.16044     9.820
ORdate_year2010                                                  1.3864     0.7213   0.17332    11.089
ORdate_year2011                                                  1.5685     0.6375   0.19296    12.750
ORdate_year2012                                                  2.7780     0.3600   0.36328    21.243
ORdate_year2013                                                  2.4359     0.4105   0.31654    18.745
ORdate_year2014                                                  1.4226     0.7029   0.17490    11.572
ORdate_year2015                                                  0.9259     1.0801   0.08387    10.220
ORdate_year2016                                                  0.9628     1.0386   0.08708    10.645
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.638  (se = 0.024 )
Likelihood ratio test= 34.99  on 18 df,   p=0.009
Wald test            = 33.16  on 18 df,   p=0.02
Score (logrank) test = 34.05  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' RASEF ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: RASEF 
Effect size...............: -0.101919 
Standard error............: 0.179336 
Odds ratio (effect size)..: 0.903 
Lower 95% CI..............: 0.635 
Upper 95% CI..............: 1.283 
T-value...................: -0.568315 
P-value...................: 0.5698214 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [NEDD4]; 7 out of 35 target-of-interest.
   > cross tabulation of NEDD4-stratum.

[5.05, 8.86) [8.86,11.49] 
         546          546 

   > fitting the model for NEDD4-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                   coef exp(coef)  se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.86,11.49] -0.130907  0.877300  0.178893 -0.732 0.464314    
Age                                                            0.037750  1.038472  0.010688  3.532 0.000412 ***
Gendermale                                                     0.512993  1.670283  0.217346  2.360 0.018262 *  
ORdate_year2002                                                1.146723  3.147859  1.073091  1.069 0.285243    
ORdate_year2003                                                0.107604  1.113606  1.097531  0.098 0.921899    
ORdate_year2004                                                0.369213  1.446596  1.062100  0.348 0.728121    
ORdate_year2005                                                0.674498  1.963048  1.038611  0.649 0.516065    
ORdate_year2006                                                0.727943  2.070816  1.033564  0.704 0.481244    
ORdate_year2007                                               -0.235851  0.789898  1.080643 -0.218 0.827234    
ORdate_year2008                                                0.805149  2.237030  1.033503  0.779 0.435951    
ORdate_year2009                                                0.247825  1.281236  1.049013  0.236 0.813242    
ORdate_year2010                                                0.350876  1.420311  1.061709  0.330 0.741036    
ORdate_year2011                                                0.473496  1.605597  1.069575  0.443 0.657986    
ORdate_year2012                                                1.046586  2.847912  1.038750  1.008 0.313674    
ORdate_year2013                                                0.913926  2.494096  1.041228  0.878 0.380085    
ORdate_year2014                                                0.374199  1.453826  1.069510  0.350 0.726430    
ORdate_year2015                                               -0.050445  0.950806  1.226301 -0.041 0.967187    
ORdate_year2016                                               -0.008294  0.991740  1.227611 -0.007 0.994609    
ORdate_year2017                                                      NA        NA  0.000000     NA       NA    
ORdate_year2018                                                      NA        NA  0.000000     NA       NA    
ORdate_year2019                                                      NA        NA  0.000000     NA       NA    
ORdate_year2020                                                      NA        NA  0.000000     NA       NA    
ORdate_year2021                                                      NA        NA  0.000000     NA       NA    
ORdate_year2022                                                      NA        NA  0.000000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.86,11.49]    0.8773     1.1399   0.61784     1.246
Age                                                              1.0385     0.9630   1.01694     1.060
Gendermale                                                       1.6703     0.5987   1.09090     2.557
ORdate_year2002                                                  3.1479     0.3177   0.38424    25.789
ORdate_year2003                                                  1.1136     0.8980   0.12957     9.571
ORdate_year2004                                                  1.4466     0.6913   0.18042    11.599
ORdate_year2005                                                  1.9630     0.5094   0.25637    15.031
ORdate_year2006                                                  2.0708     0.4829   0.27313    15.700
ORdate_year2007                                                  0.7899     1.2660   0.09500     6.568
ORdate_year2008                                                  2.2370     0.4470   0.29509    16.959
ORdate_year2009                                                  1.2812     0.7805   0.16395    10.013
ORdate_year2010                                                  1.4203     0.7041   0.17728    11.379
ORdate_year2011                                                  1.6056     0.6228   0.19734    13.064
ORdate_year2012                                                  2.8479     0.3511   0.37183    21.813
ORdate_year2013                                                  2.4941     0.4009   0.32405    19.196
ORdate_year2014                                                  1.4538     0.6878   0.17871    11.827
ORdate_year2015                                                  0.9508     1.0517   0.08595    10.518
ORdate_year2016                                                  0.9917     1.0083   0.08942    10.999
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.639  (se = 0.024 )
Likelihood ratio test= 35.21  on 18 df,   p=0.009
Wald test            = 33.47  on 18 df,   p=0.01
Score (logrank) test = 34.4  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' NEDD4 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: NEDD4 
Effect size...............: -0.130907 
Standard error............: 0.178893 
Odds ratio (effect size)..: 0.877 
Lower 95% CI..............: 0.618 
Upper 95% CI..............: 1.246 
T-value...................: -0.731761 
P-value...................: 0.4643143 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [TCL1A]; 8 out of 35 target-of-interest.
   > cross tabulation of TCL1A-stratum.

[6.16, 8.59) [8.59,11.42] 
         546          546 

   > fitting the model for TCL1A-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.59,11.42]  0.47879   1.61412  0.18761  2.552 0.010709 *  
Age                                                            0.03969   1.04049  0.01075  3.691 0.000223 ***
Gendermale                                                     0.51421   1.67231  0.21701  2.370 0.017811 *  
ORdate_year2002                                                0.88914   2.43304  1.07359  0.828 0.407559    
ORdate_year2003                                               -0.15440   0.85693  1.10025 -0.140 0.888398    
ORdate_year2004                                                0.20428   1.22664  1.06360  0.192 0.847690    
ORdate_year2005                                                0.48384   1.62229  1.04127  0.465 0.642174    
ORdate_year2006                                                0.55818   1.74748  1.03550  0.539 0.589860    
ORdate_year2007                                               -0.43479   0.64740  1.08275 -0.402 0.688008    
ORdate_year2008                                                0.66622   1.94686  1.03416  0.644 0.519439    
ORdate_year2009                                                0.14740   1.15881  1.04978  0.140 0.888339    
ORdate_year2010                                                0.17771   1.19448  1.06231  0.167 0.867145    
ORdate_year2011                                                0.31843   1.37497  1.07044  0.297 0.766105    
ORdate_year2012                                                0.96729   2.63082  1.03809  0.932 0.351441    
ORdate_year2013                                                0.86143   2.36654  1.04118  0.827 0.408035    
ORdate_year2014                                                0.42030   1.52241  1.06957  0.393 0.694351    
ORdate_year2015                                               -0.03829   0.96243  1.22522 -0.031 0.975067    
ORdate_year2016                                               -0.11375   0.89248  1.22514 -0.093 0.926025    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.59,11.42]    1.6141     0.6195   1.11749     2.331
Age                                                              1.0405     0.9611   1.01879     1.063
Gendermale                                                       1.6723     0.5980   1.09295     2.559
ORdate_year2002                                                  2.4330     0.4110   0.29670    19.952
ORdate_year2003                                                  0.8569     1.1670   0.09918     7.404
ORdate_year2004                                                  1.2266     0.8152   0.15254     9.864
ORdate_year2005                                                  1.6223     0.6164   0.21076    12.487
ORdate_year2006                                                  1.7475     0.5723   0.22961    13.299
ORdate_year2007                                                  0.6474     1.5446   0.07754     5.405
ORdate_year2008                                                  1.9469     0.5136   0.25648    14.778
ORdate_year2009                                                  1.1588     0.8630   0.14806     9.070
ORdate_year2010                                                  1.1945     0.8372   0.14892     9.581
ORdate_year2011                                                  1.3750     0.7273   0.16871    11.206
ORdate_year2012                                                  2.6308     0.3801   0.34392    20.124
ORdate_year2013                                                  2.3665     0.4226   0.30751    18.213
ORdate_year2014                                                  1.5224     0.6569   0.18712    12.387
ORdate_year2015                                                  0.9624     1.0390   0.08719    10.624
ORdate_year2016                                                  0.8925     1.1205   0.08086     9.850
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.651  (se = 0.023 )
Likelihood ratio test= 41.31  on 18 df,   p=0.001
Wald test            = 39.49  on 18 df,   p=0.002
Score (logrank) test = 40.33  on 18 df,   p=0.002


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' TCL1A ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: TCL1A 
Effect size...............: 0.478789 
Standard error............: 0.187609 
Odds ratio (effect size)..: 1.614 
Lower 95% CI..............: 1.117 
Upper 95% CI..............: 2.331 
T-value...................: 2.552057 
P-value...................: 0.0107089 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [FBXO15]; 9 out of 35 target-of-interest.
   > cross tabulation of FBXO15-stratum.

[5.12, 8.78) [8.78,11.78] 
         546          546 

   > fitting the model for FBXO15-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.78,11.78] -0.11840   0.88834  0.17850 -0.663 0.507118    
Age                                                            0.03745   1.03816  0.01071  3.495 0.000473 ***
Gendermale                                                     0.50958   1.66459  0.21751  2.343 0.019142 *  
ORdate_year2002                                                1.13146   3.10017  1.07213  1.055 0.291275    
ORdate_year2003                                                0.11356   1.12026  1.09817  0.103 0.917639    
ORdate_year2004                                                0.36830   1.44528  1.06213  0.347 0.728774    
ORdate_year2005                                                0.68272   1.97925  1.03882  0.657 0.511052    
ORdate_year2006                                                0.72828   2.07151  1.03360  0.705 0.481058    
ORdate_year2007                                               -0.23040   0.79422  1.08091 -0.213 0.831207    
ORdate_year2008                                                0.79540   2.21533  1.03322  0.770 0.441400    
ORdate_year2009                                                0.24861   1.28224  1.04903  0.237 0.812666    
ORdate_year2010                                                0.34194   1.40767  1.06138  0.322 0.747331    
ORdate_year2011                                                0.47907   1.61457  1.06999  0.448 0.654348    
ORdate_year2012                                                1.03761   2.82246  1.03842  0.999 0.317688    
ORdate_year2013                                                0.90857   2.48077  1.04112  0.873 0.382833    
ORdate_year2014                                                0.35741   1.42963  1.06932  0.334 0.738195    
ORdate_year2015                                               -0.07943   0.92364  1.22514 -0.065 0.948304    
ORdate_year2016                                               -0.03026   0.97020  1.22619 -0.025 0.980314    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.78,11.78]    0.8883     1.1257   0.62610     1.260
Age                                                              1.0382     0.9632   1.01659     1.060
Gendermale                                                       1.6646     0.6007   1.08683     2.550
ORdate_year2002                                                  3.1002     0.3226   0.37912    25.351
ORdate_year2003                                                  1.1203     0.8927   0.13018     9.640
ORdate_year2004                                                  1.4453     0.6919   0.18024    11.589
ORdate_year2005                                                  1.9792     0.5052   0.25838    15.162
ORdate_year2006                                                  2.0715     0.4827   0.27320    15.707
ORdate_year2007                                                  0.7942     1.2591   0.09547     6.607
ORdate_year2008                                                  2.2153     0.4514   0.29239    16.785
ORdate_year2009                                                  1.2822     0.7799   0.16407    10.021
ORdate_year2010                                                  1.4077     0.7104   0.17581    11.271
ORdate_year2011                                                  1.6146     0.6194   0.19828    13.147
ORdate_year2012                                                  2.8225     0.3543   0.36874    21.604
ORdate_year2013                                                  2.4808     0.4031   0.32239    19.089
ORdate_year2014                                                  1.4296     0.6995   0.17580    11.626
ORdate_year2015                                                  0.9236     1.0827   0.08369    10.194
ORdate_year2016                                                  0.9702     1.0307   0.08773    10.730
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.639  (se = 0.023 )
Likelihood ratio test= 35.11  on 18 df,   p=0.009
Wald test            = 33.39  on 18 df,   p=0.01
Score (logrank) test = 34.32  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' FBXO15 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: FBXO15 
Effect size...............: -0.118402 
Standard error............: 0.178497 
Odds ratio (effect size)..: 0.888 
Lower 95% CI..............: 0.626 
Upper 95% CI..............: 1.26 
T-value...................: -0.663332 
P-value...................: 0.507118 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [F5]; 10 out of 35 target-of-interest.
   > cross tabulation of F5-stratum.

[2.37,6.33) [6.33,8.71] 
        546         546 

   > fitting the model for F5-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.33,8.71] -0.2356596  0.7900496  0.1790296 -1.316 0.188068    
Age                                                           0.0377861  1.0385090  0.0106710  3.541 0.000399 ***
Gendermale                                                    0.5027419  1.6532481  0.2175820  2.311 0.020856 *  
ORdate_year2002                                               1.1689528  3.2186202  1.0722601  1.090 0.275635    
ORdate_year2003                                               0.1426968  1.1533801  1.0977744  0.130 0.896576    
ORdate_year2004                                               0.3765155  1.4571981  1.0619320  0.355 0.722921    
ORdate_year2005                                               0.6876809  1.9890973  1.0385962  0.662 0.507891    
ORdate_year2006                                               0.7322611  2.0797780  1.0334452  0.709 0.478596    
ORdate_year2007                                              -0.2026152  0.8165924  1.0810463 -0.187 0.851327    
ORdate_year2008                                               0.8323505  2.2987155  1.0336659  0.805 0.420680    
ORdate_year2009                                               0.2478738  1.2812983  1.0489814  0.236 0.813200    
ORdate_year2010                                               0.3694393  1.4469231  1.0614674  0.348 0.727806    
ORdate_year2011                                               0.4854269  1.6248685  1.0694406  0.454 0.649896    
ORdate_year2012                                               1.0674555  2.9079707  1.0385915  1.028 0.304048    
ORdate_year2013                                               0.9202721  2.5099733  1.0410728  0.884 0.376715    
ORdate_year2014                                               0.3880292  1.4740728  1.0694832  0.363 0.716740    
ORdate_year2015                                              -0.0093909  0.9906531  1.2266149 -0.008 0.993892    
ORdate_year2016                                               0.0007318  1.0007321  1.2259548  0.001 0.999524    
ORdate_year2017                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2018                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2019                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2020                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2021                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2022                                                      NA         NA  0.0000000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.33,8.71]    0.7900     1.2657   0.55624     1.122
Age                                                             1.0385     0.9629   1.01701     1.060
Gendermale                                                      1.6532     0.6049   1.07927     2.532
ORdate_year2002                                                 3.2186     0.3107   0.39351    26.326
ORdate_year2003                                                 1.1534     0.8670   0.13414     9.917
ORdate_year2004                                                 1.4572     0.6862   0.18180    11.680
ORdate_year2005                                                 1.9891     0.5027   0.25978    15.230
ORdate_year2006                                                 2.0798     0.4808   0.27438    15.765
ORdate_year2007                                                 0.8166     1.2246   0.09813     6.795
ORdate_year2008                                                 2.2987     0.4350   0.30313    17.432
ORdate_year2009                                                 1.2813     0.7805   0.16397    10.013
ORdate_year2010                                                 1.4469     0.6911   0.18069    11.587
ORdate_year2011                                                 1.6249     0.6154   0.19976    13.217
ORdate_year2012                                                 2.9080     0.3439   0.37979    22.266
ORdate_year2013                                                 2.5100     0.3984   0.32622    19.312
ORdate_year2014                                                 1.4741     0.6784   0.18121    11.991
ORdate_year2015                                                 0.9907     1.0094   0.08950    10.965
ORdate_year2016                                                 1.0007     0.9993   0.09053    11.062
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.642  (se = 0.023 )
Likelihood ratio test= 36.41  on 18 df,   p=0.006
Wald test            = 34.67  on 18 df,   p=0.01
Score (logrank) test = 35.59  on 18 df,   p=0.008


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' F5 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: F5 
Effect size...............: -0.23566 
Standard error............: 0.17903 
Odds ratio (effect size)..: 0.79 
Lower 95% CI..............: 0.556 
Upper 95% CI..............: 1.122 
T-value...................: -1.316316 
P-value...................: 0.188068 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [TMEM212]; 11 out of 35 target-of-interest.
   > cross tabulation of TMEM212-stratum.

[4.88, 8.16) [8.16,10.71] 
         546          546 

   > fitting the model for TMEM212-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                   coef exp(coef)  se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.16,10.71] -0.316088  0.728995  0.180987 -1.746 0.080730 .  
Age                                                            0.037439  1.038149  0.010661  3.512 0.000445 ***
Gendermale                                                     0.505209  1.657332  0.217474  2.323 0.020175 *  
ORdate_year2002                                                1.223448  3.398888  1.073155  1.140 0.254266    
ORdate_year2003                                                0.167741  1.182631  1.097757  0.153 0.878553    
ORdate_year2004                                                0.381783  1.464894  1.061813  0.360 0.719178    
ORdate_year2005                                                0.702656  2.019108  1.038606  0.677 0.498699    
ORdate_year2006                                                0.745173  2.106805  1.033452  0.721 0.470878    
ORdate_year2007                                               -0.183450  0.832394  1.081062 -0.170 0.865251    
ORdate_year2008                                                0.845820  2.329888  1.033563  0.818 0.413155    
ORdate_year2009                                                0.248004  1.281465  1.048952  0.236 0.813099    
ORdate_year2010                                                0.384127  1.468332  1.061430  0.362 0.717430    
ORdate_year2011                                                0.545729  1.725867  1.070489  0.510 0.610195    
ORdate_year2012                                                1.075935  2.932733  1.038394  1.036 0.300131    
ORdate_year2013                                                0.904388  2.470419  1.040917  0.869 0.384936    
ORdate_year2014                                                0.371520  1.449937  1.069260  0.347 0.728249    
ORdate_year2015                                               -0.015325  0.984792  1.225807 -0.013 0.990025    
ORdate_year2016                                                0.004607  1.004618  1.225538  0.004 0.997001    
ORdate_year2017                                                      NA        NA  0.000000     NA       NA    
ORdate_year2018                                                      NA        NA  0.000000     NA       NA    
ORdate_year2019                                                      NA        NA  0.000000     NA       NA    
ORdate_year2020                                                      NA        NA  0.000000     NA       NA    
ORdate_year2021                                                      NA        NA  0.000000     NA       NA    
ORdate_year2022                                                      NA        NA  0.000000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.16,10.71]    0.7290     1.3718   0.51129     1.039
Age                                                              1.0381     0.9633   1.01668     1.060
Gendermale                                                       1.6573     0.6034   1.08217     2.538
ORdate_year2002                                                  3.3989     0.2942   0.41483    27.849
ORdate_year2003                                                  1.1826     0.8456   0.13754    10.169
ORdate_year2004                                                  1.4649     0.6826   0.18281    11.739
ORdate_year2005                                                  2.0191     0.4953   0.26369    15.460
ORdate_year2006                                                  2.1068     0.4747   0.27794    15.970
ORdate_year2007                                                  0.8324     1.2014   0.10003     6.927
ORdate_year2008                                                  2.3299     0.4292   0.30730    17.665
ORdate_year2009                                                  1.2815     0.7804   0.16400    10.013
ORdate_year2010                                                  1.4683     0.6810   0.18337    11.758
ORdate_year2011                                                  1.7259     0.5794   0.21174    14.067
ORdate_year2012                                                  2.9327     0.3410   0.38317    22.447
ORdate_year2013                                                  2.4704     0.4048   0.32117    19.002
ORdate_year2014                                                  1.4499     0.6897   0.17832    11.790
ORdate_year2015                                                  0.9848     1.0154   0.08911    10.883
ORdate_year2016                                                  1.0046     0.9954   0.09095    11.096
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.645  (se = 0.023 )
Likelihood ratio test= 37.75  on 18 df,   p=0.004
Wald test            = 36.16  on 18 df,   p=0.007
Score (logrank) test = 37.11  on 18 df,   p=0.005


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' TMEM212 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: TMEM212 
Effect size...............: -0.316088 
Standard error............: 0.180987 
Odds ratio (effect size)..: 0.729 
Lower 95% CI..............: 0.511 
Upper 95% CI..............: 1.039 
T-value...................: -1.746466 
P-value...................: 0.08072998 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [PTPRD]; 12 out of 35 target-of-interest.
   > cross tabulation of PTPRD-stratum.

[2.37,4.61) [4.61,6.22] 
        546         546 

   > fitting the model for PTPRD-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                 coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.61,6.22] -0.35889   0.69845  0.18159 -1.976 0.048110 *  
Age                                                           0.03685   1.03753  0.01070  3.444 0.000573 ***
Gendermale                                                    0.52049   1.68286  0.21731  2.395 0.016612 *  
ORdate_year2002                                               1.06614   2.90415  1.07055  0.996 0.319305    
ORdate_year2003                                               0.09580   1.10054  1.09646  0.087 0.930376    
ORdate_year2004                                               0.28407   1.32853  1.06218  0.267 0.789130    
ORdate_year2005                                               0.58141   1.78856  1.03921  0.559 0.575836    
ORdate_year2006                                               0.67065   1.95552  1.03356  0.649 0.516418    
ORdate_year2007                                              -0.28229   0.75405  1.08033 -0.261 0.793862    
ORdate_year2008                                               0.75980   2.13785  1.03300  0.736 0.462018    
ORdate_year2009                                               0.20850   1.23183  1.04908  0.199 0.842460    
ORdate_year2010                                               0.27361   1.31470  1.06093  0.258 0.796486    
ORdate_year2011                                               0.49019   1.63262  1.06928  0.458 0.646647    
ORdate_year2012                                               0.97320   2.64640  1.03799  0.938 0.348458    
ORdate_year2013                                               0.86673   2.37912  1.04102  0.833 0.405082    
ORdate_year2014                                               0.39817   1.48910  1.06945  0.372 0.709658    
ORdate_year2015                                              -0.13650   0.87241  1.22508 -0.111 0.911282    
ORdate_year2016                                              -0.02881   0.97160  1.22498 -0.024 0.981236    
ORdate_year2017                                                    NA        NA  0.00000     NA       NA    
ORdate_year2018                                                    NA        NA  0.00000     NA       NA    
ORdate_year2019                                                    NA        NA  0.00000     NA       NA    
ORdate_year2020                                                    NA        NA  0.00000     NA       NA    
ORdate_year2021                                                    NA        NA  0.00000     NA       NA    
ORdate_year2022                                                    NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.61,6.22]    0.6985     1.4317   0.48929     0.997
Age                                                             1.0375     0.9638   1.01600     1.060
Gendermale                                                      1.6829     0.5942   1.09919     2.576
ORdate_year2002                                                 2.9042     0.3443   0.35626    23.674
ORdate_year2003                                                 1.1005     0.9086   0.12832     9.439
ORdate_year2004                                                 1.3285     0.7527   0.16567    10.654
ORdate_year2005                                                 1.7886     0.5591   0.23331    13.711
ORdate_year2006                                                 1.9555     0.5114   0.25792    14.826
ORdate_year2007                                                 0.7541     1.3262   0.09074     6.266
ORdate_year2008                                                 2.1378     0.4678   0.28228    16.191
ORdate_year2009                                                 1.2318     0.8118   0.15761     9.628
ORdate_year2010                                                 1.3147     0.7606   0.16435    10.517
ORdate_year2011                                                 1.6326     0.6125   0.20077    13.276
ORdate_year2012                                                 2.6464     0.3779   0.34604    20.239
ORdate_year2013                                                 2.3791     0.4203   0.30924    18.303
ORdate_year2014                                                 1.4891     0.6715   0.18307    12.113
ORdate_year2015                                                 0.8724     1.1463   0.07906     9.627
ORdate_year2016                                                 0.9716     1.0292   0.08806    10.720
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.645  (se = 0.023 )
Likelihood ratio test= 38.64  on 18 df,   p=0.003
Wald test            = 36.73  on 18 df,   p=0.006
Score (logrank) test = 37.79  on 18 df,   p=0.004


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PTPRD ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: PTPRD 
Effect size...............: -0.358888 
Standard error............: 0.181587 
Odds ratio (effect size)..: 0.698 
Lower 95% CI..............: 0.489 
Upper 95% CI..............: 0.997 
T-value...................: -1.976394 
P-value...................: 0.04811021 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [CYP46A1]; 13 out of 35 target-of-interest.
   > cross tabulation of CYP46A1-stratum.

[4.75, 8.32) [8.32,11.38] 
         546          546 

   > fitting the model for CYP46A1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.32,11.38] -0.25284   0.77659  0.18069 -1.399 0.161718    
Age                                                            0.03734   1.03805  0.01069  3.495 0.000475 ***
Gendermale                                                     0.50839   1.66262  0.21734  2.339 0.019330 *  
ORdate_year2002                                                1.19713   3.31059  1.07317  1.116 0.264635    
ORdate_year2003                                                0.15832   1.17154  1.09816  0.144 0.885367    
ORdate_year2004                                                0.39472   1.48398  1.06220  0.372 0.710183    
ORdate_year2005                                                0.69199   1.99770  1.03860  0.666 0.505236    
ORdate_year2006                                                0.74866   2.11417  1.03357  0.724 0.468853    
ORdate_year2007                                               -0.19599   0.82202  1.08111 -0.181 0.856141    
ORdate_year2008                                                0.82392   2.27941  1.03337  0.797 0.425271    
ORdate_year2009                                                0.26965   1.30950  1.04911  0.257 0.797159    
ORdate_year2010                                                0.37944   1.46147  1.06166  0.357 0.720790    
ORdate_year2011                                                0.50281   1.65335  1.06975  0.470 0.638339    
ORdate_year2012                                                1.07437   2.92816  1.03868  1.034 0.300967    
ORdate_year2013                                                0.89454   2.44620  1.04093  0.859 0.390140    
ORdate_year2014                                                0.33843   1.40274  1.06935  0.316 0.751639    
ORdate_year2015                                               -0.06746   0.93476  1.22515 -0.055 0.956086    
ORdate_year2016                                               -0.03184   0.96866  1.22513 -0.026 0.979267    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.32,11.38]    0.7766     1.2877   0.54499     1.107
Age                                                              1.0380     0.9633   1.01653     1.060
Gendermale                                                       1.6626     0.6015   1.08589     2.546
ORdate_year2002                                                  3.3106     0.3021   0.40404    27.126
ORdate_year2003                                                  1.1715     0.8536   0.13615    10.081
ORdate_year2004                                                  1.4840     0.6739   0.18505    11.901
ORdate_year2005                                                  1.9977     0.5006   0.26090    15.296
ORdate_year2006                                                  2.1142     0.4730   0.27885    16.029
ORdate_year2007                                                  0.8220     1.2165   0.09877     6.841
ORdate_year2008                                                  2.2794     0.4387   0.30076    17.275
ORdate_year2009                                                  1.3095     0.7636   0.16753    10.236
ORdate_year2010                                                  1.4615     0.6842   0.18243    11.708
ORdate_year2011                                                  1.6534     0.6048   0.20314    13.457
ORdate_year2012                                                  2.9282     0.3415   0.38235    22.425
ORdate_year2013                                                  2.4462     0.4088   0.31802    18.816
ORdate_year2014                                                  1.4027     0.7129   0.17248    11.408
ORdate_year2015                                                  0.9348     1.0698   0.08469    10.317
ORdate_year2016                                                  0.9687     1.0324   0.08777    10.691
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.644  (se = 0.023 )
Likelihood ratio test= 36.64  on 18 df,   p=0.006
Wald test            = 35.04  on 18 df,   p=0.009
Score (logrank) test = 36  on 18 df,   p=0.007


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CYP46A1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: CYP46A1 
Effect size...............: -0.252839 
Standard error............: 0.180688 
Odds ratio (effect size)..: 0.777 
Lower 95% CI..............: 0.545 
Upper 95% CI..............: 1.107 
T-value...................: -1.399315 
P-value...................: 0.1617184 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [OR2T33]; 14 out of 35 target-of-interest.
   > cross tabulation of OR2T33-stratum.

[2.37,7.05) [7.05,9.61] 
        546         546 

   > fitting the model for OR2T33-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                 coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.05,9.61] -0.15279   0.85831  0.17881 -0.854 0.392835    
Age                                                           0.03750   1.03822  0.01069  3.507 0.000453 ***
Gendermale                                                    0.51153   1.66785  0.21736  2.353 0.018602 *  
ORdate_year2002                                               1.13768   3.11951  1.07192  1.061 0.288535    
ORdate_year2003                                               0.10381   1.10939  1.09721  0.095 0.924623    
ORdate_year2004                                               0.35341   1.42392  1.06188  0.333 0.739273    
ORdate_year2005                                               0.66482   1.94414  1.03862  0.640 0.522108    
ORdate_year2006                                               0.72186   2.05827  1.03348  0.698 0.484880    
ORdate_year2007                                              -0.23324   0.79196  1.08065 -0.216 0.829116    
ORdate_year2008                                               0.78795   2.19889  1.03303  0.763 0.445605    
ORdate_year2009                                               0.24277   1.27478  1.04903  0.231 0.816985    
ORdate_year2010                                               0.33209   1.39388  1.06093  0.313 0.754265    
ORdate_year2011                                               0.47635   1.61018  1.06955  0.445 0.656051    
ORdate_year2012                                               1.03191   2.80641  1.03805  0.994 0.320184    
ORdate_year2013                                               0.88617   2.42582  1.04108  0.851 0.394655    
ORdate_year2014                                               0.35419   1.42503  1.06932  0.331 0.740471    
ORdate_year2015                                              -0.05141   0.94989  1.22595 -0.042 0.966551    
ORdate_year2016                                              -0.05187   0.94945  1.22504 -0.042 0.966226    
ORdate_year2017                                                    NA        NA  0.00000     NA       NA    
ORdate_year2018                                                    NA        NA  0.00000     NA       NA    
ORdate_year2019                                                    NA        NA  0.00000     NA       NA    
ORdate_year2020                                                    NA        NA  0.00000     NA       NA    
ORdate_year2021                                                    NA        NA  0.00000     NA       NA    
ORdate_year2022                                                    NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.05,9.61]    0.8583     1.1651   0.60457     1.219
Age                                                             1.0382     0.9632   1.01668     1.060
Gendermale                                                      1.6678     0.5996   1.08928     2.554
ORdate_year2002                                                 3.1195     0.3206   0.38165    25.498
ORdate_year2003                                                 1.1094     0.9014   0.12916     9.529
ORdate_year2004                                                 1.4239     0.7023   0.17767    11.412
ORdate_year2005                                                 1.9441     0.5144   0.25390    14.887
ORdate_year2006                                                 2.0583     0.4858   0.27152    15.603
ORdate_year2007                                                 0.7920     1.2627   0.09525     6.585
ORdate_year2008                                                 2.1989     0.4548   0.29033    16.654
ORdate_year2009                                                 1.2748     0.7845   0.16312     9.963
ORdate_year2010                                                 1.3939     0.7174   0.17425    11.150
ORdate_year2011                                                 1.6102     0.6210   0.19791    13.100
ORdate_year2012                                                 2.8064     0.3563   0.36691    21.466
ORdate_year2013                                                 2.4258     0.4122   0.31528    18.665
ORdate_year2014                                                 1.4250     0.7017   0.17523    11.589
ORdate_year2015                                                 0.9499     1.0528   0.08593    10.500
ORdate_year2016                                                 0.9495     1.0532   0.08604    10.477
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.64  (se = 0.023 )
Likelihood ratio test= 35.4  on 18 df,   p=0.008
Wald test            = 33.75  on 18 df,   p=0.01
Score (logrank) test = 34.65  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' OR2T33 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: OR2T33 
Effect size...............: -0.152788 
Standard error............: 0.178806 
Odds ratio (effect size)..: 0.858 
Lower 95% CI..............: 0.605 
Upper 95% CI..............: 1.219 
T-value...................: -0.854487 
P-value...................: 0.3928352 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [SORBS2]; 15 out of 35 target-of-interest.
   > cross tabulation of SORBS2-stratum.

[5.68, 7.38) [7.38,11.45] 
         546          546 

   > fitting the model for SORBS2-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.38,11.45] -0.28441   0.75246  0.18275 -1.556 0.119647    
Age                                                            0.03815   1.03889  0.01070  3.566 0.000362 ***
Gendermale                                                     0.48703   1.62747  0.21809  2.233 0.025538 *  
ORdate_year2002                                                1.01426   2.75732  1.07261  0.946 0.344351    
ORdate_year2003                                               -0.03290   0.96763  1.09944 -0.030 0.976126    
ORdate_year2004                                                0.25474   1.29013  1.06415  0.239 0.810806    
ORdate_year2005                                                0.55891   1.74877  1.04135  0.537 0.591464    
ORdate_year2006                                                0.58732   1.79917  1.03716  0.566 0.571204    
ORdate_year2007                                               -0.38422   0.68098  1.08387 -0.354 0.722975    
ORdate_year2008                                                0.67378   1.96164  1.03556  0.651 0.515275    
ORdate_year2009                                                0.13182   1.14091  1.05175  0.125 0.900256    
ORdate_year2010                                                0.21423   1.23891  1.06309  0.202 0.840296    
ORdate_year2011                                                0.35445   1.42540  1.07102  0.331 0.740682    
ORdate_year2012                                                0.96812   2.63300  1.03840  0.932 0.351171    
ORdate_year2013                                                0.86913   2.38483  1.04134  0.835 0.403931    
ORdate_year2014                                                0.33214   1.39395  1.06947  0.311 0.756132    
ORdate_year2015                                               -0.15562   0.85588  1.22584 -0.127 0.898981    
ORdate_year2016                                               -0.11367   0.89255  1.22532 -0.093 0.926088    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.38,11.45]    0.7525     1.3290   0.52592     1.077
Age                                                              1.0389     0.9626   1.01733     1.061
Gendermale                                                       1.6275     0.6145   1.06139     2.495
ORdate_year2002                                                  2.7573     0.3627   0.33689    22.568
ORdate_year2003                                                  0.9676     1.0334   0.11217     8.348
ORdate_year2004                                                  1.2901     0.7751   0.16026    10.386
ORdate_year2005                                                  1.7488     0.5718   0.22716    13.463
ORdate_year2006                                                  1.7992     0.5558   0.23563    13.737
ORdate_year2007                                                  0.6810     1.4685   0.08138     5.698
ORdate_year2008                                                  1.9616     0.5098   0.25772    14.931
ORdate_year2009                                                  1.1409     0.8765   0.14521     8.964
ORdate_year2010                                                  1.2389     0.8072   0.15422     9.953
ORdate_year2011                                                  1.4254     0.7016   0.17470    11.630
ORdate_year2012                                                  2.6330     0.3798   0.34400    20.153
ORdate_year2013                                                  2.3848     0.4193   0.30979    18.359
ORdate_year2014                                                  1.3939     0.7174   0.17136    11.339
ORdate_year2015                                                  0.8559     1.1684   0.07744     9.459
ORdate_year2016                                                  0.8926     1.1204   0.08084     9.854
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.644  (se = 0.023 )
Likelihood ratio test= 37.11  on 18 df,   p=0.005
Wald test            = 35.1  on 18 df,   p=0.009
Score (logrank) test = 36.05  on 18 df,   p=0.007


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' SORBS2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: SORBS2 
Effect size...............: -0.28441 
Standard error............: 0.182752 
Odds ratio (effect size)..: 0.752 
Lower 95% CI..............: 0.526 
Upper 95% CI..............: 1.077 
T-value...................: -1.556259 
P-value...................: 0.1196466 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [ITGA7]; 16 out of 35 target-of-interest.
   > cross tabulation of ITGA7-stratum.

[4.65, 8.32) [8.32,11.04] 
         546          546 

   > fitting the model for ITGA7-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.32,11.04] -0.26979   0.76354  0.18047 -1.495 0.134917    
Age                                                            0.03775   1.03847  0.01067  3.539 0.000402 ***
Gendermale                                                     0.50002   1.64876  0.21765  2.297 0.021595 *  
ORdate_year2002                                                1.13440   3.10930  1.07122  1.059 0.289610    
ORdate_year2003                                                0.10042   1.10563  1.09678  0.092 0.927049    
ORdate_year2004                                                0.33725   1.40110  1.06189  0.318 0.750788    
ORdate_year2005                                                0.63471   1.88647  1.03874  0.611 0.541173    
ORdate_year2006                                                0.69543   2.00458  1.03352  0.673 0.501025    
ORdate_year2007                                               -0.25993   0.77110  1.08043 -0.241 0.809877    
ORdate_year2008                                                0.78681   2.19638  1.03300  0.762 0.446252    
ORdate_year2009                                                0.20298   1.22504  1.04934  0.193 0.846620    
ORdate_year2010                                                0.33708   1.40086  1.06091  0.318 0.750689    
ORdate_year2011                                                0.47072   1.60114  1.06920  0.440 0.659755    
ORdate_year2012                                                1.04132   2.83294  1.03799  1.003 0.315763    
ORdate_year2013                                                0.91328   2.49248  1.04102  0.877 0.380330    
ORdate_year2014                                                0.39436   1.48343  1.06954  0.369 0.712336    
ORdate_year2015                                               -0.03660   0.96406  1.22559 -0.030 0.976176    
ORdate_year2016                                                0.01885   1.01903  1.22631  0.015 0.987734    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.32,11.04]    0.7635     1.3097   0.53606     1.088
Age                                                              1.0385     0.9630   1.01698     1.060
Gendermale                                                       1.6488     0.6065   1.07621     2.526
ORdate_year2002                                                  3.1093     0.3216   0.38092    25.380
ORdate_year2003                                                  1.1056     0.9045   0.12883     9.488
ORdate_year2004                                                  1.4011     0.7137   0.17482    11.229
ORdate_year2005                                                  1.8865     0.5301   0.24631    14.449
ORdate_year2006                                                  2.0046     0.4989   0.26442    15.197
ORdate_year2007                                                  0.7711     1.2968   0.09278     6.409
ORdate_year2008                                                  2.1964     0.4553   0.29001    16.634
ORdate_year2009                                                  1.2250     0.8163   0.15666     9.580
ORdate_year2010                                                  1.4009     0.7138   0.17512    11.206
ORdate_year2011                                                  1.6011     0.6246   0.19694    13.018
ORdate_year2012                                                  2.8329     0.3530   0.37042    21.666
ORdate_year2013                                                  2.4925     0.4012   0.32398    19.176
ORdate_year2014                                                  1.4834     0.6741   0.18234    12.069
ORdate_year2015                                                  0.9641     1.0373   0.08727    10.649
ORdate_year2016                                                  1.0190     0.9813   0.09212    11.273
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.645  (se = 0.023 )
Likelihood ratio test= 36.92  on 18 df,   p=0.005
Wald test            = 35.18  on 18 df,   p=0.009
Score (logrank) test = 36.09  on 18 df,   p=0.007


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' ITGA7 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: ITGA7 
Effect size...............: -0.269795 
Standard error............: 0.180466 
Odds ratio (effect size)..: 0.764 
Lower 95% CI..............: 0.536 
Upper 95% CI..............: 1.088 
T-value...................: -1.49499 
P-value...................: 0.1349172 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [FOS]; 17 out of 35 target-of-interest.
   > cross tabulation of FOS-stratum.

[2.37, 6.06) [6.06,11.07] 
         546          546 

   > fitting the model for FOS-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.06,11.07]  0.01989   1.02008  0.18764  0.106 0.915601    
Age                                                            0.03800   1.03873  0.01072  3.546 0.000391 ***
Gendermale                                                     0.51664   1.67638  0.21723  2.378 0.017390 *  
ORdate_year2002                                                1.10609   3.02251  1.07488  1.029 0.303464    
ORdate_year2003                                                0.08462   1.08831  1.09924  0.077 0.938638    
ORdate_year2004                                                0.36150   1.43548  1.06498  0.339 0.734277    
ORdate_year2005                                                0.67745   1.96885  1.04121  0.651 0.515283    
ORdate_year2006                                                0.73080   2.07673  1.04030  0.702 0.482377    
ORdate_year2007                                               -0.23822   0.78803  1.08901 -0.219 0.826848    
ORdate_year2008                                                0.79419   2.21264  1.03966  0.764 0.444933    
ORdate_year2009                                                0.25601   1.29177  1.05315  0.243 0.807934    
ORdate_year2010                                                0.32981   1.39071  1.06598  0.309 0.757018    
ORdate_year2011                                                0.45938   1.58310  1.07265  0.428 0.668456    
ORdate_year2012                                                1.02205   2.77888  1.04017  0.983 0.325813    
ORdate_year2013                                                0.90143   2.46313  1.04160  0.865 0.386802    
ORdate_year2014                                                0.36001   1.43335  1.06943  0.337 0.736388    
ORdate_year2015                                               -0.08639   0.91723  1.22600 -0.070 0.943822    
ORdate_year2016                                               -0.06755   0.93468  1.22492 -0.055 0.956019    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.06,11.07]    1.0201     0.9803   0.70618     1.474
Age                                                              1.0387     0.9627   1.01714     1.061
Gendermale                                                       1.6764     0.5965   1.09514     2.566
ORdate_year2002                                                  3.0225     0.3309   0.36764    24.849
ORdate_year2003                                                  1.0883     0.9189   0.12620     9.385
ORdate_year2004                                                  1.4355     0.6966   0.17803    11.575
ORdate_year2005                                                  1.9688     0.5079   0.25582    15.153
ORdate_year2006                                                  2.0767     0.4815   0.27032    15.955
ORdate_year2007                                                  0.7880     1.2690   0.09324     6.661
ORdate_year2008                                                  2.2126     0.4519   0.28837    16.977
ORdate_year2009                                                  1.2918     0.7741   0.16396    10.177
ORdate_year2010                                                  1.3907     0.7191   0.17214    11.236
ORdate_year2011                                                  1.5831     0.6317   0.19340    12.958
ORdate_year2012                                                  2.7789     0.3599   0.36181    21.343
ORdate_year2013                                                  2.4631     0.4060   0.31980    18.971
ORdate_year2014                                                  1.4334     0.6977   0.17622    11.659
ORdate_year2015                                                  0.9172     1.0902   0.08297    10.140
ORdate_year2016                                                  0.9347     1.0699   0.08472    10.311
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.637  (se = 0.023 )
Likelihood ratio test= 34.68  on 18 df,   p=0.01
Wald test            = 32.8  on 18 df,   p=0.02
Score (logrank) test = 33.72  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' FOS ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: FOS 
Effect size...............: 0.019886 
Standard error............: 0.187643 
Odds ratio (effect size)..: 1.02 
Lower 95% CI..............: 0.706 
Upper 95% CI..............: 1.474 
T-value...................: 0.105977 
P-value...................: 0.9156006 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [HMOX1]; 18 out of 35 target-of-interest.
   > cross tabulation of HMOX1-stratum.

[4.36, 7.02) [7.02,11.50] 
         546          546 

   > fitting the model for HMOX1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.02,11.50]  0.12686   1.13525  0.17822  0.712 0.476585    
Age                                                            0.03774   1.03846  0.01070  3.528 0.000418 ***
Gendermale                                                     0.51196   1.66855  0.21732  2.356 0.018485 *  
ORdate_year2002                                                1.11313   3.04387  1.07109  1.039 0.298690    
ORdate_year2003                                                0.08211   1.08558  1.09679  0.075 0.940322    
ORdate_year2004                                                0.34934   1.41813  1.06188  0.329 0.742168    
ORdate_year2005                                                0.66577   1.94600  1.03863  0.641 0.521517    
ORdate_year2006                                                0.72028   2.05501  1.03348  0.697 0.485837    
ORdate_year2007                                               -0.24104   0.78581  1.08050 -0.223 0.823471    
ORdate_year2008                                                0.77782   2.17672  1.03303  0.753 0.451480    
ORdate_year2009                                                0.23528   1.26527  1.04913  0.224 0.822551    
ORdate_year2010                                                0.32137   1.37901  1.06080  0.303 0.761928    
ORdate_year2011                                                0.46632   1.59411  1.06934  0.436 0.662779    
ORdate_year2012                                                1.01366   2.75567  1.03786  0.977 0.328727    
ORdate_year2013                                                0.87239   2.39263  1.04159  0.838 0.402280    
ORdate_year2014                                                0.33804   1.40219  1.06971  0.316 0.751996    
ORdate_year2015                                               -0.07951   0.92356  1.22511 -0.065 0.948250    
ORdate_year2016                                               -0.07265   0.92992  1.22492 -0.059 0.952704    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.02,11.50]    1.1353     0.8809   0.80056     1.610
Age                                                              1.0385     0.9630   1.01692     1.060
Gendermale                                                       1.6686     0.5993   1.08982     2.555
ORdate_year2002                                                  3.0439     0.3285   0.37300    24.839
ORdate_year2003                                                  1.0856     0.9212   0.12649     9.316
ORdate_year2004                                                  1.4181     0.7052   0.17695    11.366
ORdate_year2005                                                  1.9460     0.5139   0.25413    14.901
ORdate_year2006                                                  2.0550     0.4866   0.27109    15.578
ORdate_year2007                                                  0.7858     1.2726   0.09454     6.532
ORdate_year2008                                                  2.1767     0.4594   0.28740    16.486
ORdate_year2009                                                  1.2653     0.7903   0.16187     9.890
ORdate_year2010                                                  1.3790     0.7252   0.17243    11.029
ORdate_year2011                                                  1.5941     0.6273   0.19602    12.964
ORdate_year2012                                                  2.7557     0.3629   0.36041    21.070
ORdate_year2013                                                  2.3926     0.4180   0.31065    18.428
ORdate_year2014                                                  1.4022     0.7132   0.17229    11.411
ORdate_year2015                                                  0.9236     1.0828   0.08369    10.193
ORdate_year2016                                                  0.9299     1.0754   0.08429    10.259
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.639  (se = 0.024 )
Likelihood ratio test= 35.18  on 18 df,   p=0.009
Wald test            = 33.4  on 18 df,   p=0.01
Score (logrank) test = 34.29  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HMOX1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: HMOX1 
Effect size...............: 0.126856 
Standard error............: 0.178218 
Odds ratio (effect size)..: 1.135 
Lower 95% CI..............: 0.801 
Upper 95% CI..............: 1.61 
T-value...................: 0.711805 
P-value...................: 0.4765854 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [LAPTM5]; 19 out of 35 target-of-interest.
   > cross tabulation of LAPTM5-stratum.

[2.37, 6.71) [6.71,11.74] 
         546          546 

   > fitting the model for LAPTM5-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.71,11.74]  0.31680   1.37273  0.18175  1.743 0.081328 .  
Age                                                            0.03917   1.03995  0.01074  3.646 0.000267 ***
Gendermale                                                     0.51103   1.66701  0.21721  2.353 0.018635 *  
ORdate_year2002                                                1.18072   3.25673  1.07172  1.102 0.270588    
ORdate_year2003                                                0.15095   1.16294  1.09757  0.138 0.890611    
ORdate_year2004                                                0.40655   1.50162  1.06234  0.383 0.701950    
ORdate_year2005                                                0.72205   2.05864  1.03905  0.695 0.487109    
ORdate_year2006                                                0.81134   2.25092  1.03481  0.784 0.433011    
ORdate_year2007                                               -0.11460   0.89173  1.08330 -0.106 0.915754    
ORdate_year2008                                                0.88137   2.41421  1.03449  0.852 0.394223    
ORdate_year2009                                                0.32714   1.38700  1.05002  0.312 0.755376    
ORdate_year2010                                                0.38421   1.46846  1.06141  0.362 0.717362    
ORdate_year2011                                                0.52799   1.69551  1.06996  0.493 0.621687    
ORdate_year2012                                                1.13411   3.10840  1.04018  1.090 0.275582    
ORdate_year2013                                                0.89995   2.45948  1.04101  0.864 0.387314    
ORdate_year2014                                                0.34773   1.41586  1.06935  0.325 0.745042    
ORdate_year2015                                               -0.00618   0.99384  1.22600 -0.005 0.995978    
ORdate_year2016                                               -0.02270   0.97756  1.22515 -0.019 0.985219    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.71,11.74]    1.3727     0.7285   0.96134     1.960
Age                                                              1.0399     0.9616   1.01828     1.062
Gendermale                                                       1.6670     0.5999   1.08906     2.552
ORdate_year2002                                                  3.2567     0.3071   0.39860    26.609
ORdate_year2003                                                  1.1629     0.8599   0.13530     9.996
ORdate_year2004                                                  1.5016     0.6659   0.18720    12.046
ORdate_year2005                                                  2.0586     0.4858   0.26862    15.777
ORdate_year2006                                                  2.2509     0.4443   0.29616    17.108
ORdate_year2007                                                  0.8917     1.1214   0.10669     7.453
ORdate_year2008                                                  2.4142     0.4142   0.31784    18.337
ORdate_year2009                                                  1.3870     0.7210   0.17713    10.861
ORdate_year2010                                                  1.4685     0.6810   0.18340    11.758
ORdate_year2011                                                  1.6955     0.5898   0.20823    13.806
ORdate_year2012                                                  3.1084     0.3217   0.40470    23.875
ORdate_year2013                                                  2.4595     0.4066   0.31969    18.921
ORdate_year2014                                                  1.4159     0.7063   0.17410    11.515
ORdate_year2015                                                  0.9938     1.0062   0.08990    10.987
ORdate_year2016                                                  0.9776     1.0230   0.08857    10.789
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.644  (se = 0.024 )
Likelihood ratio test= 37.73  on 18 df,   p=0.004
Wald test            = 35.77  on 18 df,   p=0.008
Score (logrank) test = 36.72  on 18 df,   p=0.006


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LAPTM5 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: LAPTM5 
Effect size...............: 0.316802 
Standard error............: 0.181753 
Odds ratio (effect size)..: 1.373 
Lower 95% CI..............: 0.961 
Upper 95% CI..............: 1.96 
T-value...................: 1.743032 
P-value...................: 0.08132802 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [MMP9]; 20 out of 35 target-of-interest.
   > cross tabulation of MMP9-stratum.

[2.37, 6.12) [6.12,12.12] 
         546          546 

   > fitting the model for MMP9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.12,12.12]  0.28365   1.32797  0.17935  1.582  0.11375    
Age                                                            0.03795   1.03868  0.01070  3.547  0.00039 ***
Gendermale                                                     0.50497   1.65693  0.21735  2.323  0.02016 *  
ORdate_year2002                                                1.12951   3.09413  1.07068  1.055  0.29145    
ORdate_year2003                                                0.13739   1.14728  1.09738  0.125  0.90037    
ORdate_year2004                                                0.36455   1.43986  1.06191  0.343  0.73138    
ORdate_year2005                                                0.68058   1.97503  1.03853  0.655  0.51226    
ORdate_year2006                                                0.75628   2.13034  1.03366  0.732  0.46438    
ORdate_year2007                                               -0.17693   0.83784  1.08146 -0.164  0.87004    
ORdate_year2008                                                0.81847   2.26703  1.03321  0.792  0.42827    
ORdate_year2009                                                0.26855   1.30807  1.04912  0.256  0.79797    
ORdate_year2010                                                0.32875   1.38922  1.06081  0.310  0.75664    
ORdate_year2011                                                0.47221   1.60353  1.06919  0.442  0.65874    
ORdate_year2012                                                1.03621   2.81852  1.03803  0.998  0.31816    
ORdate_year2013                                                0.87334   2.39491  1.04105  0.839  0.40152    
ORdate_year2014                                                0.32304   1.38132  1.06946  0.302  0.76261    
ORdate_year2015                                               -0.07281   0.92978  1.22513 -0.059  0.95261    
ORdate_year2016                                               -0.09082   0.91318  1.22500 -0.074  0.94090    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.12,12.12]    1.3280     0.7530   0.93438     1.887
Age                                                              1.0387     0.9628   1.01713     1.061
Gendermale                                                       1.6569     0.6035   1.08217     2.537
ORdate_year2002                                                  3.0941     0.3232   0.37947    25.229
ORdate_year2003                                                  1.1473     0.8716   0.13353     9.857
ORdate_year2004                                                  1.4399     0.6945   0.17965    11.540
ORdate_year2005                                                  1.9750     0.5063   0.25797    15.121
ORdate_year2006                                                  2.1303     0.4694   0.28093    16.155
ORdate_year2007                                                  0.8378     1.1936   0.10060     6.978
ORdate_year2008                                                  2.2670     0.4411   0.29922    17.176
ORdate_year2009                                                  1.3081     0.7645   0.16735    10.225
ORdate_year2010                                                  1.3892     0.7198   0.17370    11.111
ORdate_year2011                                                  1.6035     0.6236   0.19724    13.037
ORdate_year2012                                                  2.8185     0.3548   0.36851    21.557
ORdate_year2013                                                  2.3949     0.4176   0.31127    18.426
ORdate_year2014                                                  1.3813     0.7239   0.16981    11.236
ORdate_year2015                                                  0.9298     1.0755   0.08425    10.262
ORdate_year2016                                                  0.9132     1.0951   0.08276    10.076
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.642  (se = 0.024 )
Likelihood ratio test= 37.19  on 18 df,   p=0.005
Wald test            = 35.46  on 18 df,   p=0.008
Score (logrank) test = 36.44  on 18 df,   p=0.006


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MMP9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: MMP9 
Effect size...............: 0.28365 
Standard error............: 0.17935 
Odds ratio (effect size)..: 1.328 
Lower 95% CI..............: 0.934 
Upper 95% CI..............: 1.887 
T-value...................: 1.581543 
P-value...................: 0.1137539 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [SIGLEC1]; 21 out of 35 target-of-interest.
   > cross tabulation of SIGLEC1-stratum.

[2.37,5.42) [5.42,9.88] 
        546         546 

   > fitting the model for SIGLEC1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef)  se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.42,9.88]  0.292954  1.340381  0.181083  1.618 0.105708    
Age                                                           0.038434  1.039183  0.010713  3.588 0.000334 ***
Gendermale                                                    0.501711  1.651545  0.217478  2.307 0.021057 *  
ORdate_year2002                                               1.199421  3.318196  1.072562  1.118 0.263449    
ORdate_year2003                                               0.151899  1.164043  1.097558  0.138 0.889926    
ORdate_year2004                                               0.409212  1.505630  1.062321  0.385 0.700085    
ORdate_year2005                                               0.730342  2.075790  1.039184  0.703 0.482178    
ORdate_year2006                                               0.800042  2.225634  1.034562  0.773 0.439336    
ORdate_year2007                                              -0.136117  0.872741  1.082662 -0.126 0.899950    
ORdate_year2008                                               0.869123  2.384819  1.034314  0.840 0.400746    
ORdate_year2009                                               0.326565  1.386198  1.050124  0.311 0.755818    
ORdate_year2010                                               0.389101  1.475654  1.061574  0.367 0.713968    
ORdate_year2011                                               0.523742  1.688333  1.069960  0.489 0.624490    
ORdate_year2012                                               1.109702  3.033455  1.039426  1.068 0.285696    
ORdate_year2013                                               0.902549  2.465879  1.040970  0.867 0.385928    
ORdate_year2014                                               0.335993  1.399330  1.069394  0.314 0.753376    
ORdate_year2015                                              -0.003658  0.996349  1.226120 -0.003 0.997620    
ORdate_year2016                                              -0.041922  0.958944  1.224963 -0.034 0.972699    
ORdate_year2017                                                     NA        NA  0.000000     NA       NA    
ORdate_year2018                                                     NA        NA  0.000000     NA       NA    
ORdate_year2019                                                     NA        NA  0.000000     NA       NA    
ORdate_year2020                                                     NA        NA  0.000000     NA       NA    
ORdate_year2021                                                     NA        NA  0.000000     NA       NA    
ORdate_year2022                                                     NA        NA  0.000000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.42,9.88]    1.3404     0.7461   0.93992     1.911
Age                                                             1.0392     0.9623   1.01759     1.061
Gendermale                                                      1.6515     0.6055   1.07838     2.529
ORdate_year2002                                                 3.3182     0.3014   0.40545    27.156
ORdate_year2003                                                 1.1640     0.8591   0.13543    10.005
ORdate_year2004                                                 1.5056     0.6642   0.18770    12.077
ORdate_year2005                                                 2.0758     0.4817   0.27079    15.912
ORdate_year2006                                                 2.2256     0.4493   0.29298    16.907
ORdate_year2007                                                 0.8727     1.1458   0.10455     7.285
ORdate_year2008                                                 2.3848     0.4193   0.31408    18.108
ORdate_year2009                                                 1.3862     0.7214   0.17699    10.857
ORdate_year2010                                                 1.4757     0.6777   0.18423    11.819
ORdate_year2011                                                 1.6883     0.5923   0.20735    13.747
ORdate_year2012                                                 3.0335     0.3297   0.39553    23.265
ORdate_year2013                                                 2.4659     0.4055   0.32055    18.969
ORdate_year2014                                                 1.3993     0.7146   0.17205    11.381
ORdate_year2015                                                 0.9963     1.0037   0.09010    11.018
ORdate_year2016                                                 0.9589     1.0428   0.08692    10.580
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.645  (se = 0.024 )
Likelihood ratio test= 37.3  on 18 df,   p=0.005
Wald test            = 35.66  on 18 df,   p=0.008
Score (logrank) test = 36.57  on 18 df,   p=0.006


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' SIGLEC1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: SIGLEC1 
Effect size...............: 0.292954 
Standard error............: 0.181083 
Odds ratio (effect size)..: 1.34 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 1.911 
T-value...................: 1.617791 
P-value...................: 0.1057077 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [FTL]; 22 out of 35 target-of-interest.
   > cross tabulation of FTL-stratum.

[3.23, 9.66) [9.66,13.76] 
         546          546 

   > fitting the model for FTL-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][9.66,13.76]  0.11510   1.12199  0.17925  0.642 0.520785    
Age                                                            0.03825   1.03899  0.01071  3.570 0.000356 ***
Gendermale                                                     0.51093   1.66684  0.21737  2.350 0.018749 *  
ORdate_year2002                                                1.11595   3.05247  1.07121  1.042 0.297519    
ORdate_year2003                                                0.09401   1.09857  1.09714  0.086 0.931716    
ORdate_year2004                                                0.36553   1.44127  1.06213  0.344 0.730740    
ORdate_year2005                                                0.67109   1.95637  1.03867  0.646 0.518213    
ORdate_year2006                                                0.73478   2.08503  1.03384  0.711 0.477251    
ORdate_year2007                                               -0.22204   0.80088  1.08148 -0.205 0.837330    
ORdate_year2008                                                0.79833   2.22182  1.03335  0.773 0.439780    
ORdate_year2009                                                0.25811   1.29448  1.04922  0.246 0.805682    
ORdate_year2010                                                0.32632   1.38586  1.06087  0.308 0.758391    
ORdate_year2011                                                0.46215   1.58748  1.06927  0.432 0.665590    
ORdate_year2012                                                1.03082   2.80335  1.03820  0.993 0.320767    
ORdate_year2013                                                0.88487   2.42266  1.04121  0.850 0.395412    
ORdate_year2014                                                0.33733   1.40120  1.06982  0.315 0.752522    
ORdate_year2015                                               -0.08560   0.91796  1.22505 -0.070 0.944296    
ORdate_year2016                                               -0.06688   0.93531  1.22491 -0.055 0.956456    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][9.66,13.76]    1.1220     0.8913   0.78961     1.594
Age                                                              1.0390     0.9625   1.01740     1.061
Gendermale                                                       1.6668     0.5999   1.08859     2.552
ORdate_year2002                                                  3.0525     0.3276   0.37397    24.915
ORdate_year2003                                                  1.0986     0.9103   0.12792     9.434
ORdate_year2004                                                  1.4413     0.6938   0.17974    11.557
ORdate_year2005                                                  1.9564     0.5112   0.25546    14.982
ORdate_year2006                                                  2.0850     0.4796   0.27486    15.817
ORdate_year2007                                                  0.8009     1.2486   0.09616     6.670
ORdate_year2008                                                  2.2218     0.4501   0.29317    16.838
ORdate_year2009                                                  1.2945     0.7725   0.16558    10.120
ORdate_year2010                                                  1.3859     0.7216   0.17326    11.085
ORdate_year2011                                                  1.5875     0.6299   0.19523    12.908
ORdate_year2012                                                  2.8034     0.3567   0.36640    21.449
ORdate_year2013                                                  2.4227     0.4128   0.31478    18.646
ORdate_year2014                                                  1.4012     0.7137   0.17214    11.406
ORdate_year2015                                                  0.9180     1.0894   0.08319    10.130
ORdate_year2016                                                  0.9353     1.0692   0.08478    10.318
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.639  (se = 0.024 )
Likelihood ratio test= 35.08  on 18 df,   p=0.009
Wald test            = 33.31  on 18 df,   p=0.02
Score (logrank) test = 34.22  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' FTL ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: FTL 
Effect size...............: 0.115102 
Standard error............: 0.179248 
Odds ratio (effect size)..: 1.122 
Lower 95% CI..............: 0.79 
Upper 95% CI..............: 1.594 
T-value...................: 0.642136 
P-value...................: 0.5207848 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [CD14]; 23 out of 35 target-of-interest.
   > cross tabulation of CD14-stratum.

[2.37, 6.73) [6.73,11.30] 
         546          546 

   > fitting the model for CD14-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                   coef exp(coef)  se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.73,11.30]  0.394225  1.483234  0.181432  2.173 0.029791 *  
Age                                                            0.039536  1.040327  0.010720  3.688 0.000226 ***
Gendermale                                                     0.500590  1.649695  0.217316  2.304 0.021250 *  
ORdate_year2002                                                1.246242  3.477249  1.072699  1.162 0.245324    
ORdate_year2003                                                0.170718  1.186156  1.097339  0.156 0.876368    
ORdate_year2004                                                0.439778  1.552363  1.062398  0.414 0.678912    
ORdate_year2005                                                0.749417  2.115765  1.039120  0.721 0.470785    
ORdate_year2006                                                0.851270  2.342621  1.035039  0.822 0.410819    
ORdate_year2007                                               -0.092398  0.911743  1.082704 -0.085 0.931991    
ORdate_year2008                                                0.906406  2.475409  1.034418  0.876 0.380896    
ORdate_year2009                                                0.359030  1.431940  1.050180  0.342 0.732445    
ORdate_year2010                                                0.402280  1.495230  1.061311  0.379 0.704658    
ORdate_year2011                                                0.551831  1.736430  1.069955  0.516 0.606028    
ORdate_year2012                                                1.122621  3.072898  1.038944  1.081 0.279902    
ORdate_year2013                                                0.919722  2.508594  1.040989  0.884 0.376962    
ORdate_year2014                                                0.344594  1.411417  1.069316  0.322 0.747258    
ORdate_year2015                                                0.007913  1.007945  1.225754  0.006 0.994849    
ORdate_year2016                                               -0.032784  0.967747  1.224947 -0.027 0.978648    
ORdate_year2017                                                      NA        NA  0.000000     NA       NA    
ORdate_year2018                                                      NA        NA  0.000000     NA       NA    
ORdate_year2019                                                      NA        NA  0.000000     NA       NA    
ORdate_year2020                                                      NA        NA  0.000000     NA       NA    
ORdate_year2021                                                      NA        NA  0.000000     NA       NA    
ORdate_year2022                                                      NA        NA  0.000000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.73,11.30]    1.4832     0.6742   1.03938     2.117
Age                                                              1.0403     0.9612   1.01870     1.062
Gendermale                                                       1.6497     0.6062   1.07752     2.526
ORdate_year2002                                                  3.4772     0.2876   0.42477    28.465
ORdate_year2003                                                  1.1862     0.8431   0.13807    10.191
ORdate_year2004                                                  1.5524     0.6442   0.19350    12.454
ORdate_year2005                                                  2.1158     0.4726   0.27604    16.217
ORdate_year2006                                                  2.3426     0.4269   0.30809    17.813
ORdate_year2007                                                  0.9117     1.0968   0.10921     7.612
ORdate_year2008                                                  2.4754     0.4040   0.32595    18.799
ORdate_year2009                                                  1.4319     0.6984   0.18281    11.216
ORdate_year2010                                                  1.4952     0.6688   0.18677    11.970
ORdate_year2011                                                  1.7364     0.5759   0.21326    14.139
ORdate_year2012                                                  3.0729     0.3254   0.40105    23.545
ORdate_year2013                                                  2.5086     0.3986   0.32609    19.298
ORdate_year2014                                                  1.4114     0.7085   0.17356    11.478
ORdate_year2015                                                  1.0079     0.9921   0.09122    11.138
ORdate_year2016                                                  0.9677     1.0333   0.08772    10.677
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.652  (se = 0.024 )
Likelihood ratio test= 39.44  on 18 df,   p=0.002
Wald test            = 37.96  on 18 df,   p=0.004
Score (logrank) test = 38.84  on 18 df,   p=0.003


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD14 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: CD14 
Effect size...............: 0.394225 
Standard error............: 0.181432 
Odds ratio (effect size)..: 1.483 
Lower 95% CI..............: 1.039 
Upper 95% CI..............: 2.117 
T-value...................: 2.172854 
P-value...................: 0.02979134 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [HCST]; 24 out of 35 target-of-interest.
   > cross tabulation of HCST-stratum.

[2.37, 7.02) [7.02,11.97] 
         546          546 

   > fitting the model for HCST-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.02,11.97]  0.27794   1.32041  0.18018  1.543 0.122932    
Age                                                            0.03875   1.03951  0.01071  3.617 0.000298 ***
Gendermale                                                     0.50816   1.66224  0.21732  2.338 0.019371 *  
ORdate_year2002                                                1.15259   3.16639  1.07119  1.076 0.281931    
ORdate_year2003                                                0.09927   1.10436  1.09664  0.091 0.927873    
ORdate_year2004                                                0.36742   1.44401  1.06172  0.346 0.729295    
ORdate_year2005                                                0.68158   1.97700  1.03850  0.656 0.511621    
ORdate_year2006                                                0.77075   2.16138  1.03387  0.745 0.455973    
ORdate_year2007                                               -0.16830   0.84510  1.08172 -0.156 0.876362    
ORdate_year2008                                                0.82781   2.28830  1.03335  0.801 0.423079    
ORdate_year2009                                                0.27926   1.32215  1.04919  0.266 0.790113    
ORdate_year2010                                                0.33431   1.39698  1.06078  0.315 0.752641    
ORdate_year2011                                                0.49284   1.63696  1.06942  0.461 0.644907    
ORdate_year2012                                                1.06419   2.89848  1.03834  1.025 0.305414    
ORdate_year2013                                                0.87792   2.40588  1.04100  0.843 0.399038    
ORdate_year2014                                                0.31424   1.36922  1.06960  0.294 0.768917    
ORdate_year2015                                               -0.04787   0.95326  1.22528 -0.039 0.968834    
ORdate_year2016                                               -0.08260   0.92072  1.22489 -0.067 0.946235    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.02,11.97]    1.3204     0.7573   0.92755     1.880
Age                                                              1.0395     0.9620   1.01791     1.062
Gendermale                                                       1.6622     0.6016   1.08570     2.545
ORdate_year2002                                                  3.1664     0.3158   0.38794    25.844
ORdate_year2003                                                  1.1044     0.9055   0.12872     9.475
ORdate_year2004                                                  1.4440     0.6925   0.18023    11.569
ORdate_year2005                                                  1.9770     0.5058   0.25825    15.135
ORdate_year2006                                                  2.1614     0.4627   0.28490    16.397
ORdate_year2007                                                  0.8451     1.1833   0.10142     7.042
ORdate_year2008                                                  2.2883     0.4370   0.30194    17.342
ORdate_year2009                                                  1.3221     0.7563   0.16913    10.336
ORdate_year2010                                                  1.3970     0.7158   0.17468    11.172
ORdate_year2011                                                  1.6370     0.6109   0.20126    13.315
ORdate_year2012                                                  2.8985     0.3450   0.37873    22.182
ORdate_year2013                                                  2.4059     0.4156   0.31273    18.509
ORdate_year2014                                                  1.3692     0.7303   0.16828    11.141
ORdate_year2015                                                  0.9533     1.0490   0.08635    10.524
ORdate_year2016                                                  0.9207     1.0861   0.08346    10.157
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.645  (se = 0.024 )
Likelihood ratio test= 37.06  on 18 df,   p=0.005
Wald test            = 35.38  on 18 df,   p=0.008
Score (logrank) test = 36.26  on 18 df,   p=0.007


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HCST ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: HCST 
Effect size...............: 0.277942 
Standard error............: 0.180179 
Odds ratio (effect size)..: 1.32 
Lower 95% CI..............: 0.928 
Upper 95% CI..............: 1.88 
T-value...................: 1.542584 
P-value...................: 0.1229317 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [TIMP3]; 25 out of 35 target-of-interest.
   > cross tabulation of TIMP3-stratum.

[2.37, 6.14) [6.14,11.90] 
         546          546 

   > fitting the model for TIMP3-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.14,11.90]  0.19323   1.21316  0.18585  1.040 0.298472    
Age                                                            0.03831   1.03906  0.01073  3.571 0.000356 ***
Gendermale                                                     0.52826   1.69597  0.21748  2.429 0.015142 *  
ORdate_year2002                                                1.19471   3.30261  1.07481  1.112 0.266330    
ORdate_year2003                                                0.19450   1.21470  1.10255  0.176 0.859973    
ORdate_year2004                                                0.44509   1.56064  1.06552  0.418 0.676147    
ORdate_year2005                                                0.75494   2.12748  1.04176  0.725 0.468653    
ORdate_year2006                                                0.81863   2.26739  1.03783  0.789 0.430237    
ORdate_year2007                                               -0.13364   0.87490  1.08624 -0.123 0.902081    
ORdate_year2008                                                0.88218   2.41615  1.03736  0.850 0.395099    
ORdate_year2009                                                0.32631   1.38584  1.05173  0.310 0.756363    
ORdate_year2010                                                0.38262   1.46613  1.06242  0.360 0.718739    
ORdate_year2011                                                0.51766   1.67810  1.07090  0.483 0.628820    
ORdate_year2012                                                1.06461   2.89972  1.03892  1.025 0.305490    
ORdate_year2013                                                0.91651   2.50056  1.04120  0.880 0.378726    
ORdate_year2014                                                0.36915   1.44650  1.06945  0.345 0.729963    
ORdate_year2015                                               -0.01497   0.98514  1.22708 -0.012 0.990267    
ORdate_year2016                                               -0.04529   0.95572  1.22507 -0.037 0.970508    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.14,11.90]    1.2132     0.8243   0.84280     1.746
Age                                                              1.0391     0.9624   1.01743     1.061
Gendermale                                                       1.6960     0.5896   1.10738     2.597
ORdate_year2002                                                  3.3026     0.3028   0.40177    27.148
ORdate_year2003                                                  1.2147     0.8232   0.13995    10.543
ORdate_year2004                                                  1.5606     0.6408   0.19335    12.597
ORdate_year2005                                                  2.1275     0.4700   0.27613    16.391
ORdate_year2006                                                  2.2674     0.4410   0.29656    17.335
ORdate_year2007                                                  0.8749     1.1430   0.10407     7.355
ORdate_year2008                                                  2.4162     0.4139   0.31632    18.455
ORdate_year2009                                                  1.3858     0.7216   0.17639    10.888
ORdate_year2010                                                  1.4661     0.6821   0.18274    11.763
ORdate_year2011                                                  1.6781     0.5959   0.20571    13.689
ORdate_year2012                                                  2.8997     0.3449   0.37847    22.217
ORdate_year2013                                                  2.5006     0.3999   0.32491    19.245
ORdate_year2014                                                  1.4465     0.6913   0.17783    11.766
ORdate_year2015                                                  0.9851     1.0151   0.08892    10.914
ORdate_year2016                                                  0.9557     1.0463   0.08661    10.547
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.638  (se = 0.024 )
Likelihood ratio test= 35.75  on 18 df,   p=0.008
Wald test            = 33.76  on 18 df,   p=0.01
Score (logrank) test = 34.7  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' TIMP3 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: TIMP3 
Effect size...............: 0.193227 
Standard error............: 0.185846 
Odds ratio (effect size)..: 1.213 
Lower 95% CI..............: 0.843 
Upper 95% CI..............: 1.746 
T-value...................: 1.039715 
P-value...................: 0.2984721 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [CCL2]; 26 out of 35 target-of-interest.
   > cross tabulation of CCL2-stratum.

[2.37, 6.27) [6.27,10.61] 
         546          546 

   > fitting the model for CCL2-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.27,10.61]  0.12358   1.13155  0.18156  0.681 0.496078    
Age                                                            0.03812   1.03886  0.01071  3.558 0.000373 ***
Gendermale                                                     0.51677   1.67660  0.21717  2.379 0.017337 *  
ORdate_year2002                                                1.15113   3.16178  1.07386  1.072 0.283739    
ORdate_year2003                                                0.12270   1.13054  1.09890  0.112 0.911098    
ORdate_year2004                                                0.38233   1.46570  1.06279  0.360 0.719039    
ORdate_year2005                                                0.70318   2.02016  1.03987  0.676 0.498903    
ORdate_year2006                                                0.77257   2.16533  1.03658  0.745 0.456087    
ORdate_year2007                                               -0.18288   0.83287  1.08520 -0.169 0.866171    
ORdate_year2008                                                0.83444   2.30353  1.03590  0.806 0.420519    
ORdate_year2009                                                0.29230   1.33950  1.05120  0.278 0.780968    
ORdate_year2010                                                0.36135   1.43526  1.06258  0.340 0.733807    
ORdate_year2011                                                0.49427   1.63930  1.07099  0.462 0.644436    
ORdate_year2012                                                1.04799   2.85190  1.03900  1.009 0.313142    
ORdate_year2013                                                0.90636   2.47530  1.04113  0.871 0.383998    
ORdate_year2014                                                0.37048   1.44842  1.06952  0.346 0.729047    
ORdate_year2015                                               -0.04629   0.95477  1.22673 -0.038 0.969902    
ORdate_year2016                                               -0.05520   0.94630  1.22503 -0.045 0.964060    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.27,10.61]    1.1315     0.8837   0.79273     1.615
Age                                                              1.0389     0.9626   1.01727     1.061
Gendermale                                                       1.6766     0.5964   1.09539     2.566
ORdate_year2002                                                  3.1618     0.3163   0.38535    25.942
ORdate_year2003                                                  1.1305     0.8845   0.13119     9.742
ORdate_year2004                                                  1.4657     0.6823   0.18256    11.768
ORdate_year2005                                                  2.0202     0.4950   0.26318    15.507
ORdate_year2006                                                  2.1653     0.4618   0.28391    16.514
ORdate_year2007                                                  0.8329     1.2007   0.09928     6.987
ORdate_year2008                                                  2.3035     0.4341   0.30243    17.545
ORdate_year2009                                                  1.3395     0.7465   0.17067    10.513
ORdate_year2010                                                  1.4353     0.6967   0.17884    11.519
ORdate_year2011                                                  1.6393     0.6100   0.20092    13.375
ORdate_year2012                                                  2.8519     0.3506   0.37217    21.854
ORdate_year2013                                                  2.4753     0.4040   0.32167    19.048
ORdate_year2014                                                  1.4484     0.6904   0.17804    11.784
ORdate_year2015                                                  0.9548     1.0474   0.08624    10.570
ORdate_year2016                                                  0.9463     1.0568   0.08576    10.442
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.639  (se = 0.024 )
Likelihood ratio test= 35.14  on 18 df,   p=0.009
Wald test            = 33.32  on 18 df,   p=0.02
Score (logrank) test = 34.22  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CCL2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: CCL2 
Effect size...............: 0.123585 
Standard error............: 0.181563 
Odds ratio (effect size)..: 1.132 
Lower 95% CI..............: 0.793 
Upper 95% CI..............: 1.615 
T-value...................: 0.680673 
P-value...................: 0.4960783 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [SAT1]; 27 out of 35 target-of-interest.
   > cross tabulation of SAT1-stratum.

[2.37, 7.73) [7.73,12.14] 
         546          546 

   > fitting the model for SAT1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.73,12.14]  0.14766   1.15912  0.18027  0.819 0.412727    
Age                                                            0.03828   1.03902  0.01071  3.574 0.000352 ***
Gendermale                                                     0.51557   1.67459  0.21719  2.374 0.017607 *  
ORdate_year2002                                                1.15269   3.16670  1.07302  1.074 0.282711    
ORdate_year2003                                                0.11952   1.12695  1.09797  0.109 0.913318    
ORdate_year2004                                                0.39095   1.47839  1.06285  0.368 0.712998    
ORdate_year2005                                                0.70148   2.01674  1.03936  0.675 0.499726    
ORdate_year2006                                                0.75823   2.13450  1.03461  0.733 0.463642    
ORdate_year2007                                               -0.18799   0.82862  1.08324 -0.174 0.862224    
ORdate_year2008                                                0.82574   2.28357  1.03440  0.798 0.424707    
ORdate_year2009                                                0.28413   1.32861  1.05005  0.271 0.786707    
ORdate_year2010                                                0.35337   1.42386  1.06158  0.333 0.739229    
ORdate_year2011                                                0.49862   1.64644  1.07068  0.466 0.641430    
ORdate_year2012                                                1.05031   2.85854  1.03875  1.011 0.311956    
ORdate_year2013                                                0.89692   2.45204  1.04102  0.862 0.388918    
ORdate_year2014                                                0.35506   1.42627  1.06935  0.332 0.739863    
ORdate_year2015                                               -0.05109   0.95019  1.22595 -0.042 0.966757    
ORdate_year2016                                               -0.06598   0.93615  1.22489 -0.054 0.957045    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.73,12.14]    1.1591     0.8627   0.81411     1.650
Age                                                              1.0390     0.9624   1.01743     1.061
Gendermale                                                       1.6746     0.5972   1.09404     2.563
ORdate_year2002                                                  3.1667     0.3158   0.38659    25.940
ORdate_year2003                                                  1.1270     0.8873   0.13101     9.694
ORdate_year2004                                                  1.4784     0.6764   0.18411    11.871
ORdate_year2005                                                  2.0167     0.4958   0.26300    15.465
ORdate_year2006                                                  2.1345     0.4685   0.28095    16.217
ORdate_year2007                                                  0.8286     1.2068   0.09915     6.925
ORdate_year2008                                                  2.2836     0.4379   0.30070    17.342
ORdate_year2009                                                  1.3286     0.7527   0.16967    10.404
ORdate_year2010                                                  1.4239     0.7023   0.17776    11.405
ORdate_year2011                                                  1.6464     0.6074   0.20192    13.425
ORdate_year2012                                                  2.8585     0.3498   0.37321    21.894
ORdate_year2013                                                  2.4520     0.4078   0.31872    18.864
ORdate_year2014                                                  1.4263     0.7011   0.17538    11.599
ORdate_year2015                                                  0.9502     1.0524   0.08596    10.504
ORdate_year2016                                                  0.9362     1.0682   0.08486    10.327
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.64  (se = 0.024 )
Likelihood ratio test= 35.34  on 18 df,   p=0.009
Wald test            = 33.56  on 18 df,   p=0.01
Score (logrank) test = 34.46  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' SAT1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: SAT1 
Effect size...............: 0.147657 
Standard error............: 0.180267 
Odds ratio (effect size)..: 1.159 
Lower 95% CI..............: 0.814 
Upper 95% CI..............: 1.65 
T-value...................: 0.819104 
P-value...................: 0.4127268 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [CD163]; 28 out of 35 target-of-interest.
   > cross tabulation of CD163-stratum.

[2.37, 5.74) [5.74,10.50] 
         546          546 

   > fitting the model for CD163-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.74,10.50]  0.26232   1.29994  0.18168  1.444 0.148785    
Age                                                            0.03859   1.03935  0.01070  3.605 0.000312 ***
Gendermale                                                     0.50782   1.66166  0.21726  2.337 0.019419 *  
ORdate_year2002                                                1.19618   3.30746  1.07294  1.115 0.264911    
ORdate_year2003                                                0.17195   1.18761  1.09868  0.157 0.875636    
ORdate_year2004                                                0.44864   1.56618  1.06386  0.422 0.673237    
ORdate_year2005                                                0.75227   2.12181  1.04018  0.723 0.469550    
ORdate_year2006                                                0.83416   2.30288  1.03651  0.805 0.420947    
ORdate_year2007                                               -0.11721   0.88939  1.08428 -0.108 0.913914    
ORdate_year2008                                                0.88963   2.43424  1.03559  0.859 0.390308    
ORdate_year2009                                                0.34428   1.41098  1.05112  0.328 0.743261    
ORdate_year2010                                                0.39443   1.48354  1.06195  0.371 0.710324    
ORdate_year2011                                                0.55857   1.74816  1.07155  0.521 0.602181    
ORdate_year2012                                                1.09647   2.99359  1.03931  1.055 0.291426    
ORdate_year2013                                                0.92316   2.51723  1.04117  0.887 0.375264    
ORdate_year2014                                                0.36800   1.44485  1.06944  0.344 0.730764    
ORdate_year2015                                               -0.02604   0.97430  1.22574 -0.021 0.983052    
ORdate_year2016                                               -0.06132   0.94052  1.22492 -0.050 0.960071    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.74,10.50]    1.2999     0.7693   0.91049     1.856
Age                                                              1.0393     0.9621   1.01777     1.061
Gendermale                                                       1.6617     0.6018   1.08545     2.544
ORdate_year2002                                                  3.3075     0.3023   0.40384    27.088
ORdate_year2003                                                  1.1876     0.8420   0.13787    10.230
ORdate_year2004                                                  1.5662     0.6385   0.19466    12.601
ORdate_year2005                                                  2.1218     0.4713   0.27625    16.297
ORdate_year2006                                                  2.3029     0.4342   0.30199    17.561
ORdate_year2007                                                  0.8894     1.1244   0.10621     7.448
ORdate_year2008                                                  2.4342     0.4108   0.31979    18.529
ORdate_year2009                                                  1.4110     0.7087   0.17981    11.072
ORdate_year2010                                                  1.4835     0.6741   0.18508    11.892
ORdate_year2011                                                  1.7482     0.5720   0.21403    14.279
ORdate_year2012                                                  2.9936     0.3340   0.39042    22.954
ORdate_year2013                                                  2.5172     0.3973   0.32710    19.372
ORdate_year2014                                                  1.4448     0.6921   0.17763    11.753
ORdate_year2015                                                  0.9743     1.0264   0.08817    10.766
ORdate_year2016                                                  0.9405     1.0632   0.08525    10.376
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.642  (se = 0.024 )
Likelihood ratio test= 36.76  on 18 df,   p=0.006
Wald test            = 35.07  on 18 df,   p=0.009
Score (logrank) test = 35.85  on 18 df,   p=0.007


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD163 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: CD163 
Effect size...............: 0.262321 
Standard error............: 0.181683 
Odds ratio (effect size)..: 1.3 
Lower 95% CI..............: 0.91 
Upper 95% CI..............: 1.856 
T-value...................: 1.443838 
P-value...................: 0.1487847 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [PTGDS]; 29 out of 35 target-of-interest.
   > cross tabulation of PTGDS-stratum.

[2.37,4.58) [4.58,9.91] 
        546         546 

   > fitting the model for PTGDS-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                 coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.58,9.91]  0.13968   1.14991  0.18139  0.770 0.441249    
Age                                                           0.03803   1.03876  0.01070  3.553 0.000381 ***
Gendermale                                                    0.51418   1.67227  0.21732  2.366 0.017979 *  
ORdate_year2002                                               1.13932   3.12463  1.07218  1.063 0.287957    
ORdate_year2003                                               0.10658   1.11246  1.09735  0.097 0.922630    
ORdate_year2004                                               0.37789   1.45921  1.06236  0.356 0.722055    
ORdate_year2005                                               0.68745   1.98863  1.03888  0.662 0.508153    
ORdate_year2006                                               0.75781   2.13360  1.03473  0.732 0.463939    
ORdate_year2007                                              -0.20115   0.81779  1.08246 -0.186 0.852580    
ORdate_year2008                                               0.81567   2.26068  1.03393  0.789 0.430172    
ORdate_year2009                                               0.25722   1.29333  1.04913  0.245 0.806323    
ORdate_year2010                                               0.33560   1.39878  1.06100  0.316 0.751770    
ORdate_year2011                                               0.46799   1.59678  1.06934  0.438 0.661643    
ORdate_year2012                                               1.04864   2.85377  1.03880  1.009 0.312748    
ORdate_year2013                                               0.88556   2.42434  1.04113  0.851 0.395004    
ORdate_year2014                                               0.34217   1.40799  1.06951  0.320 0.749023    
ORdate_year2015                                              -0.05287   0.94851  1.22603 -0.043 0.965606    
ORdate_year2016                                              -0.05576   0.94577  1.22500 -0.046 0.963696    
ORdate_year2017                                                    NA        NA  0.00000     NA       NA    
ORdate_year2018                                                    NA        NA  0.00000     NA       NA    
ORdate_year2019                                                    NA        NA  0.00000     NA       NA    
ORdate_year2020                                                    NA        NA  0.00000     NA       NA    
ORdate_year2021                                                    NA        NA  0.00000     NA       NA    
ORdate_year2022                                                    NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.58,9.91]    1.1499     0.8696   0.80587     1.641
Age                                                             1.0388     0.9627   1.01719     1.061
Gendermale                                                      1.6723     0.5980   1.09226     2.560
ORdate_year2002                                                 3.1246     0.3200   0.38208    25.553
ORdate_year2003                                                 1.1125     0.8989   0.12949     9.558
ORdate_year2004                                                 1.4592     0.6853   0.18190    11.706
ORdate_year2005                                                 1.9886     0.5029   0.25957    15.235
ORdate_year2006                                                 2.1336     0.4687   0.28077    16.213
ORdate_year2007                                                 0.8178     1.2228   0.09801     6.824
ORdate_year2008                                                 2.2607     0.4423   0.29796    17.152
ORdate_year2009                                                 1.2933     0.7732   0.16546    10.109
ORdate_year2010                                                 1.3988     0.7149   0.17483    11.191
ORdate_year2011                                                 1.5968     0.6263   0.19635    12.986
ORdate_year2012                                                 2.8538     0.3504   0.37255    21.860
ORdate_year2013                                                 2.4243     0.4125   0.31505    18.655
ORdate_year2014                                                 1.4080     0.7102   0.17307    11.454
ORdate_year2015                                                 0.9485     1.0543   0.08579    10.487
ORdate_year2016                                                 0.9458     1.0573   0.08572    10.435
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.639  (se = 0.023 )
Likelihood ratio test= 35.26  on 18 df,   p=0.009
Wald test            = 33.41  on 18 df,   p=0.01
Score (logrank) test = 34.32  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PTGDS ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: PTGDS 
Effect size...............: 0.139684 
Standard error............: 0.181388 
Odds ratio (effect size)..: 1.15 
Lower 95% CI..............: 0.806 
Upper 95% CI..............: 1.641 
T-value...................: 0.770085 
P-value...................: 0.4412494 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [LGALS9]; 30 out of 35 target-of-interest.
   > cross tabulation of LGALS9-stratum.

[2.37, 6.27) [6.27,10.57] 
         546          546 

   > fitting the model for LGALS9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.27,10.57]  0.32306   1.38135  0.18081  1.787 0.073987 .  
Age                                                            0.03868   1.03944  0.01074  3.603 0.000314 ***
Gendermale                                                     0.50366   1.65477  0.21732  2.318 0.020469 *  
ORdate_year2002                                                1.16932   3.21980  1.07130  1.091 0.275053    
ORdate_year2003                                                0.11137   1.11781  1.09687  0.102 0.919125    
ORdate_year2004                                                0.37222   1.45096  1.06189  0.351 0.725942    
ORdate_year2005                                                0.67422   1.96251  1.03856  0.649 0.516216    
ORdate_year2006                                                0.77961   2.18062  1.03393  0.754 0.450834    
ORdate_year2007                                               -0.15906   0.85294  1.08174 -0.147 0.883096    
ORdate_year2008                                                0.83158   2.29695  1.03330  0.805 0.420946    
ORdate_year2009                                                0.27298   1.31387  1.04915  0.260 0.794719    
ORdate_year2010                                                0.33036   1.39147  1.06082  0.311 0.755481    
ORdate_year2011                                                0.48231   1.61981  1.06928  0.451 0.651946    
ORdate_year2012                                                1.04828   2.85274  1.03811  1.010 0.312591    
ORdate_year2013                                                0.86877   2.38397  1.04105  0.835 0.403993    
ORdate_year2014                                                0.30116   1.35142  1.06967  0.282 0.778296    
ORdate_year2015                                               -0.05234   0.94901  1.22529 -0.043 0.965928    
ORdate_year2016                                               -0.07124   0.93124  1.22490 -0.058 0.953623    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.27,10.57]    1.3813     0.7239   0.96916     1.969
Age                                                              1.0394     0.9621   1.01780     1.062
Gendermale                                                       1.6548     0.6043   1.08083     2.533
ORdate_year2002                                                  3.2198     0.3106   0.39440    26.286
ORdate_year2003                                                  1.1178     0.8946   0.13023     9.595
ORdate_year2004                                                  1.4510     0.6892   0.18104    11.629
ORdate_year2005                                                  1.9625     0.5096   0.25632    15.026
ORdate_year2006                                                  2.1806     0.4586   0.28741    16.545
ORdate_year2007                                                  0.8529     1.1724   0.10236     7.107
ORdate_year2008                                                  2.2969     0.4354   0.30311    17.406
ORdate_year2009                                                  1.3139     0.7611   0.16808    10.270
ORdate_year2010                                                  1.3915     0.7187   0.17398    11.129
ORdate_year2011                                                  1.6198     0.6174   0.19920    13.172
ORdate_year2012                                                  2.8527     0.3505   0.37293    21.822
ORdate_year2013                                                  2.3840     0.4195   0.30986    18.342
ORdate_year2014                                                  1.3514     0.7400   0.16607    10.997
ORdate_year2015                                                  0.9490     1.0537   0.08596    10.477
ORdate_year2016                                                  0.9312     1.0738   0.08442    10.273
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.646  (se = 0.024 )
Likelihood ratio test= 37.89  on 18 df,   p=0.004
Wald test            = 36.29  on 18 df,   p=0.006
Score (logrank) test = 37.18  on 18 df,   p=0.005


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LGALS9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: LGALS9 
Effect size...............: 0.323059 
Standard error............: 0.180814 
Odds ratio (effect size)..: 1.381 
Lower 95% CI..............: 0.969 
Upper 95% CI..............: 1.969 
T-value...................: 1.786694 
P-value...................: 0.07398703 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [ACKR1]; 31 out of 35 target-of-interest.
   > cross tabulation of ACKR1-stratum.

[2.37, 4.26) [4.26,11.26] 
         546          546 

   > fitting the model for ACKR1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                  coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.26,11.26]  0.23703   1.26748  0.18092  1.310 0.190153    
Age                                                            0.03803   1.03876  0.01068  3.562 0.000367 ***
Gendermale                                                     0.50652   1.65951  0.21737  2.330 0.019796 *  
ORdate_year2002                                                1.18682   3.27663  1.07295  1.106 0.268671    
ORdate_year2003                                                0.14201   1.15258  1.09772  0.129 0.897069    
ORdate_year2004                                                0.39114   1.47867  1.06218  0.368 0.712690    
ORdate_year2005                                                0.69651   2.00674  1.03880  0.670 0.502544    
ORdate_year2006                                                0.78099   2.18364  1.03452  0.755 0.450291    
ORdate_year2007                                               -0.16827   0.84512  1.08223 -0.155 0.876436    
ORdate_year2008                                                0.82627   2.28478  1.03360  0.799 0.424053    
ORdate_year2009                                                0.29050   1.33710  1.04956  0.277 0.781947    
ORdate_year2010                                                0.34699   1.41481  1.06096  0.327 0.743626    
ORdate_year2011                                                0.50896   1.66356  1.06998  0.476 0.634306    
ORdate_year2012                                                1.04809   2.85219  1.03815  1.010 0.312699    
ORdate_year2013                                                0.89382   2.44446  1.04103  0.859 0.390562    
ORdate_year2014                                                0.34451   1.41130  1.06940  0.322 0.747339    
ORdate_year2015                                               -0.05404   0.94739  1.22526 -0.044 0.964818    
ORdate_year2016                                               -0.08547   0.91808  1.22492 -0.070 0.944373    
ORdate_year2017                                                     NA        NA  0.00000     NA       NA    
ORdate_year2018                                                     NA        NA  0.00000     NA       NA    
ORdate_year2019                                                     NA        NA  0.00000     NA       NA    
ORdate_year2020                                                     NA        NA  0.00000     NA       NA    
ORdate_year2021                                                     NA        NA  0.00000     NA       NA    
ORdate_year2022                                                     NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.26,11.26]    1.2675     0.7890   0.88908     1.807
Age                                                              1.0388     0.9627   1.01725     1.061
Gendermale                                                       1.6595     0.6026   1.08381     2.541
ORdate_year2002                                                  3.2766     0.3052   0.40007    26.836
ORdate_year2003                                                  1.1526     0.8676   0.13406     9.909
ORdate_year2004                                                  1.4787     0.6763   0.18439    11.858
ORdate_year2005                                                  2.0067     0.4983   0.26197    15.372
ORdate_year2006                                                  2.1836     0.4580   0.28747    16.587
ORdate_year2007                                                  0.8451     1.1833   0.10133     7.049
ORdate_year2008                                                  2.2848     0.4377   0.30133    17.324
ORdate_year2009                                                  1.3371     0.7479   0.17091    10.461
ORdate_year2010                                                  1.4148     0.7068   0.17685    11.319
ORdate_year2011                                                  1.6636     0.6011   0.20430    13.546
ORdate_year2012                                                  2.8522     0.3506   0.37283    21.820
ORdate_year2013                                                  2.4445     0.4091   0.31773    18.806
ORdate_year2014                                                  1.4113     0.7086   0.17352    11.479
ORdate_year2015                                                  0.9474     1.0555   0.08582    10.459
ORdate_year2016                                                  0.9181     1.0892   0.08322    10.128
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.641  (se = 0.024 )
Likelihood ratio test= 36.4  on 18 df,   p=0.006
Wald test            = 34.66  on 18 df,   p=0.01
Score (logrank) test = 35.43  on 18 df,   p=0.008


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' ACKR1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: ACKR1 
Effect size...............: 0.237034 
Standard error............: 0.180924 
Odds ratio (effect size)..: 1.267 
Lower 95% CI..............: 0.889 
Upper 95% CI..............: 1.807 
T-value...................: 1.310127 
P-value...................: 0.190153 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [NT5DC2]; 32 out of 35 target-of-interest.
   > cross tabulation of NT5DC2-stratum.

[2.37,5.51) [5.51,9.43] 
        546         546 

   > fitting the model for NT5DC2-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.51,9.43]  0.2360891  1.2662871  0.1856791  1.271 0.203554    
Age                                                           0.0382261  1.0389662  0.0107198  3.566 0.000363 ***
Gendermale                                                    0.5269552  1.6937673  0.2174326  2.424 0.015370 *  
ORdate_year2002                                               1.2120744  3.3604483  1.0744111  1.128 0.259265    
ORdate_year2003                                               0.1602660  1.1738230  1.0985539  0.146 0.884010    
ORdate_year2004                                               0.4121447  1.5100530  1.0628464  0.388 0.698183    
ORdate_year2005                                               0.7192638  2.0529213  1.0392652  0.692 0.488882    
ORdate_year2006                                               0.8247201  2.2812422  1.0366918  0.796 0.426305    
ORdate_year2007                                              -0.1222404  0.8849356  1.0851314 -0.113 0.910308    
ORdate_year2008                                               0.8914375  2.4386326  1.0364448  0.860 0.389739    
ORdate_year2009                                               0.3302861  1.3913661  1.0509926  0.314 0.753323    
ORdate_year2010                                               0.3720879  1.4507605  1.0615234  0.351 0.725947    
ORdate_year2011                                               0.5293811  1.6978812  1.0707697  0.494 0.621029    
ORdate_year2012                                               1.0855325  2.9610160  1.0392607  1.045 0.296243    
ORdate_year2013                                               0.9036118  2.4685029  1.0410177  0.868 0.385390    
ORdate_year2014                                               0.3449864  1.4119707  1.0693699  0.323 0.746993    
ORdate_year2015                                              -0.0002587  0.9997414  1.2269989  0.000 0.999832    
ORdate_year2016                                              -0.0433390  0.9575868  1.2250726 -0.035 0.971779    
ORdate_year2017                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2018                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2019                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2020                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2021                                                      NA         NA  0.0000000     NA       NA    
ORdate_year2022                                                      NA         NA  0.0000000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.51,9.43]    1.2663     0.7897   0.88000     1.822
Age                                                             1.0390     0.9625   1.01736     1.061
Gendermale                                                      1.6938     0.5904   1.10605     2.594
ORdate_year2002                                                 3.3604     0.2976   0.40913    27.602
ORdate_year2003                                                 1.1738     0.8519   0.13631    10.109
ORdate_year2004                                                 1.5101     0.6622   0.18806    12.125
ORdate_year2005                                                 2.0529     0.4871   0.26776    15.740
ORdate_year2006                                                 2.2812     0.4384   0.29905    17.402
ORdate_year2007                                                 0.8849     1.1300   0.10550     7.423
ORdate_year2008                                                 2.4386     0.4101   0.31983    18.594
ORdate_year2009                                                 1.3914     0.7187   0.17735    10.916
ORdate_year2010                                                 1.4508     0.6893   0.18114    11.619
ORdate_year2011                                                 1.6979     0.5890   0.20819    13.847
ORdate_year2012                                                 2.9610     0.3377   0.38621    22.702
ORdate_year2013                                                 2.4685     0.4051   0.32086    18.991
ORdate_year2014                                                 1.4120     0.7082   0.17361    11.484
ORdate_year2015                                                 0.9997     1.0003   0.09025    11.074
ORdate_year2016                                                 0.9576     1.0443   0.08677    10.567
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.641  (se = 0.024 )
Likelihood ratio test= 36.29  on 18 df,   p=0.006
Wald test            = 34.41  on 18 df,   p=0.01
Score (logrank) test = 35.28  on 18 df,   p=0.009


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' NT5DC2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: NT5DC2 
Effect size...............: 0.236089 
Standard error............: 0.185679 
Odds ratio (effect size)..: 1.266 
Lower 95% CI..............: 0.88 
Upper 95% CI..............: 1.822 
T-value...................: 1.27149 
P-value...................: 0.2035544 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [TGFBI]; 33 out of 35 target-of-interest.
   > cross tabulation of TGFBI-stratum.

[2.37, 6.4) [6.40,10.8] 
        546         546 

   > fitting the model for TGFBI-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                 coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.40,10.8]  0.17433   1.19045  0.18457  0.945 0.344912    
Age                                                           0.03837   1.03911  0.01073  3.576 0.000349 ***
Gendermale                                                    0.52347   1.68788  0.21732  2.409 0.016008 *  
ORdate_year2002                                               1.17605   3.24156  1.07402  1.095 0.273518    
ORdate_year2003                                               0.14722   1.15861  1.09921  0.134 0.893456    
ORdate_year2004                                               0.40705   1.50238  1.06340  0.383 0.701880    
ORdate_year2005                                               0.72411   2.06290  1.04020  0.696 0.486348    
ORdate_year2006                                               0.79782   2.22069  1.03682  0.769 0.441603    
ORdate_year2007                                              -0.14869   0.86184  1.08595 -0.137 0.891094    
ORdate_year2008                                               0.85066   2.34119  1.03550  0.821 0.411365    
ORdate_year2009                                               0.30641   1.35854  1.05089  0.292 0.770612    
ORdate_year2010                                               0.35923   1.43223  1.06158  0.338 0.735069    
ORdate_year2011                                               0.51775   1.67825  1.07137  0.483 0.628911    
ORdate_year2012                                               1.04381   2.84003  1.03830  1.005 0.314749    
ORdate_year2013                                               0.90317   2.46742  1.04103  0.868 0.385627    
ORdate_year2014                                               0.36211   1.43636  1.06934  0.339 0.734888    
ORdate_year2015                                              -0.04140   0.95944  1.22610 -0.034 0.973064    
ORdate_year2016                                              -0.06445   0.93759  1.22491 -0.053 0.958040    
ORdate_year2017                                                    NA        NA  0.00000     NA       NA    
ORdate_year2018                                                    NA        NA  0.00000     NA       NA    
ORdate_year2019                                                    NA        NA  0.00000     NA       NA    
ORdate_year2020                                                    NA        NA  0.00000     NA       NA    
ORdate_year2021                                                    NA        NA  0.00000     NA       NA    
ORdate_year2022                                                    NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.40,10.8]    1.1905     0.8400   0.82909     1.709
Age                                                             1.0391     0.9624   1.01749     1.061
Gendermale                                                      1.6879     0.5925   1.10244     2.584
ORdate_year2002                                                 3.2416     0.3085   0.39495    26.605
ORdate_year2003                                                 1.1586     0.8631   0.13437     9.990
ORdate_year2004                                                 1.5024     0.6656   0.18690    12.077
ORdate_year2005                                                 2.0629     0.4848   0.26857    15.845
ORdate_year2006                                                 2.2207     0.4503   0.29104    16.944
ORdate_year2007                                                 0.8618     1.1603   0.10258     7.241
ORdate_year2008                                                 2.3412     0.4271   0.30762    17.818
ORdate_year2009                                                 1.3585     0.7361   0.17320    10.656
ORdate_year2010                                                 1.4322     0.6982   0.17881    11.472
ORdate_year2011                                                 1.6783     0.5959   0.20554    13.703
ORdate_year2012                                                 2.8400     0.3521   0.37112    21.733
ORdate_year2013                                                 2.4674     0.4053   0.32071    18.983
ORdate_year2014                                                 1.4364     0.6962   0.17662    11.681
ORdate_year2015                                                 0.9594     1.0423   0.08677    10.609
ORdate_year2016                                                 0.9376     1.0666   0.08499    10.343
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.64  (se = 0.024 )
Likelihood ratio test= 35.56  on 18 df,   p=0.008
Wald test            = 33.67  on 18 df,   p=0.01
Score (logrank) test = 34.58  on 18 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' TGFBI ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: TGFBI 
Effect size...............: 0.174331 
Standard error............: 0.184574 
Odds ratio (effect size)..: 1.19 
Lower 95% CI..............: 0.829 
Upper 95% CI..............: 1.709 
T-value...................: 0.944504 
P-value...................: 0.344912 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [C1QC]; 34 out of 35 target-of-interest.
   > cross tabulation of C1QC-stratum.

[2.37, 6.64) [6.64,11.51] 
         546          546 

   > fitting the model for C1QC-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.64,11.51]  0.3116092  1.3656208  0.1811519  1.720 0.085405 .  
Age                                                            0.0383634  1.0391087  0.0106884  3.589 0.000332 ***
Gendermale                                                     0.5007131  1.6498973  0.2174346  2.303 0.021289 *  
ORdate_year2002                                                1.2052872  3.3377174  1.0725150  1.124 0.261100    
ORdate_year2003                                                0.1516531  1.1637565  1.0974767  0.138 0.890095    
ORdate_year2004                                                0.4149084  1.5142321  1.0623427  0.391 0.696123    
ORdate_year2005                                                0.7132771  2.0406678  1.0387745  0.687 0.492302    
ORdate_year2006                                                0.8188700  2.2679356  1.0349755  0.791 0.428829    
ORdate_year2007                                               -0.1340219  0.8745709  1.0824447 -0.124 0.901462    
ORdate_year2008                                                0.8739234  2.3962940  1.0342806  0.845 0.398134    
ORdate_year2009                                                0.3331312  1.3953303  1.0501464  0.317 0.751074    
ORdate_year2010                                                0.3878005  1.4737357  1.0614156  0.365 0.714842    
ORdate_year2011                                                0.5314600  1.7014146  1.0700218  0.497 0.619414    
ORdate_year2012                                                1.1056328  3.0211356  1.0391195  1.064 0.287324    
ORdate_year2013                                                0.8995266  2.4584391  1.0409660  0.864 0.387518    
ORdate_year2014                                                0.3563765  1.4281451  1.0692878  0.333 0.738920    
ORdate_year2015                                               -0.0002588  0.9997412  1.2260598  0.000 0.999832    
ORdate_year2016                                               -0.0513180  0.9499765  1.2248895 -0.042 0.966582    
ORdate_year2017                                                       NA         NA  0.0000000     NA       NA    
ORdate_year2018                                                       NA         NA  0.0000000     NA       NA    
ORdate_year2019                                                       NA         NA  0.0000000     NA       NA    
ORdate_year2020                                                       NA         NA  0.0000000     NA       NA    
ORdate_year2021                                                       NA         NA  0.0000000     NA       NA    
ORdate_year2022                                                       NA         NA  0.0000000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.64,11.51]    1.3656     0.7323   0.95749     1.948
Age                                                              1.0391     0.9624   1.01757     1.061
Gendermale                                                       1.6499     0.6061   1.07740     2.527
ORdate_year2002                                                  3.3377     0.2996   0.40787    27.313
ORdate_year2003                                                  1.1638     0.8593   0.13542    10.001
ORdate_year2004                                                  1.5142     0.6604   0.18877    12.147
ORdate_year2005                                                  2.0407     0.4900   0.26642    15.631
ORdate_year2006                                                  2.2679     0.4409   0.29830    17.243
ORdate_year2007                                                  0.8746     1.1434   0.10481     7.297
ORdate_year2008                                                  2.3963     0.4173   0.31562    18.194
ORdate_year2009                                                  1.3953     0.7167   0.17815    10.929
ORdate_year2010                                                  1.4737     0.6785   0.18405    11.800
ORdate_year2011                                                  1.7014     0.5877   0.20893    13.855
ORdate_year2012                                                  3.0211     0.3310   0.39416    23.156
ORdate_year2013                                                  2.4584     0.4068   0.31959    18.912
ORdate_year2014                                                  1.4281     0.7002   0.17563    11.613
ORdate_year2015                                                  0.9997     1.0003   0.09042    11.054
ORdate_year2016                                                  0.9500     1.0527   0.08612    10.480
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA

Concordance= 0.644  (se = 0.024 )
Likelihood ratio test= 37.65  on 18 df,   p=0.004
Wald test            = 36.01  on 18 df,   p=0.007
Score (logrank) test = 36.86  on 18 df,   p=0.005


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' C1QC ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: C1QC 
Effect size...............: 0.311609 
Standard error............: 0.181152 
Odds ratio (effect size)..: 1.366 
Lower 95% CI..............: 0.957 
Upper 95% CI..............: 1.948 
T-value...................: 1.720154 
P-value...................: 0.0854045 
Sample size in model......: 1077 
Number of events..........: 131 
   > processing [S100A9]; 35 out of 35 target-of-interest.
   > cross tabulation of S100A9-stratum.

[2.37,4.36) [4.36,9.23] 
        546         546 

   > fitting the model for S100A9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year, data = TEMP.DF)

  n= 1077, number of events= 131 
   (15 observations deleted due to missingness)

                                                                 coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.36,9.23]  0.31588   1.37146  0.18178  1.738 0.082268 .  
Age                                                           0.03875   1.03951  0.01070  3.619 0.000295 ***
Gendermale                                                    0.49856   1.64635  0.21747  2.293 0.021871 *  
ORdate_year2002                                               1.19630   3.30785  1.07203  1.116 0.264457    
ORdate_year2003                                               0.18892   1.20794  1.09843  0.172 0.863446    
ORdate_year2004                                               0.43279   1.54155  1.06269  0.407 0.683822    
ORdate_year2005                                               0.72282   2.06024  1.03898  0.696 0.486613    
ORdate_year2006                                               0.79853   2.22228  1.03432  0.772 0.440093    
ORdate_year2007                                              -0.11274   0.89338  1.08323 -0.104 0.917105    
ORdate_year2008                                               0.88501   2.42301  1.03459  0.855 0.392317    
ORdate_year2009                                               0.33434   1.39702  1.05016  0.318 0.750201    
ORdate_year2010                                               0.38219   1.46549  1.06132  0.360 0.718769    
ORdate_year2011                                               0.54248   1.72027  1.07030  0.507 0.612261    
ORdate_year2012                                               1.06779   2.90893  1.03826  1.028 0.303743    
ORdate_year2013                                               0.91280   2.49128  1.04102  0.877 0.380577    
ORdate_year2014                                               0.34416   1.41080  1.06933  0.322 0.747570    
ORdate_year2015                                              -0.01314   0.98694  1.22571 -0.011 0.991445    
ORdate_year2016                                              -0.06304   0.93891  1.22486 -0.051 0.958954    
ORdate_year2017                                                    NA        NA  0.00000     NA       NA    
ORdate_year2018                                                    NA        NA  0.00000     NA       NA    
ORdate_year2019                                                    NA        NA  0.00000     NA       NA    
ORdate_year2020                                                    NA        NA  0.00000     NA       NA    
ORdate_year2021                                                    NA        NA  0.00000     NA       NA    
ORdate_year2022                                                    NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.36,9.23]    1.3715     0.7291   0.96040     1.958
Age                                                             1.0395     0.9620   1.01792     1.062
Gendermale                                                      1.6464     0.6074   1.07502     2.521
ORdate_year2002                                                 3.3079     0.3023   0.40460    27.043
ORdate_year2003                                                 1.2079     0.8279   0.14030    10.400
ORdate_year2004                                                 1.5415     0.6487   0.19204    12.374
ORdate_year2005                                                 2.0602     0.4854   0.26887    15.787
ORdate_year2006                                                 2.2223     0.4500   0.29267    16.874
ORdate_year2007                                                 0.8934     1.1193   0.10690     7.466
ORdate_year2008                                                 2.4230     0.4127   0.31894    18.408
ORdate_year2009                                                 1.3970     0.7158   0.17836    10.942
ORdate_year2010                                                 1.4655     0.6824   0.18305    11.732
ORdate_year2011                                                 1.7203     0.5813   0.21113    14.016
ORdate_year2012                                                 2.9089     0.3438   0.38016    22.259
ORdate_year2013                                                 2.4913     0.4014   0.32382    19.166
ORdate_year2014                                                 1.4108     0.7088   0.17348    11.473
ORdate_year2015                                                 0.9869     1.0132   0.08932    10.905
ORdate_year2016                                                 0.9389     1.0651   0.08512    10.357
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA

Concordance= 0.646  (se = 0.024 )
Likelihood ratio test= 37.72  on 18 df,   p=0.004
Wald test            = 36.11  on 18 df,   p=0.007
Score (logrank) test = 36.87  on 18 df,   p=0.005


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' S100A9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: S100A9 
Effect size...............: 0.315879 
Standard error............: 0.181782 
Odds ratio (effect size)..: 1.371 
Lower 95% CI..............: 0.96 
Upper 95% CI..............: 1.958 
T-value...................: 1.737674 
P-value...................: 0.08226828 
Sample size in model......: 1077 
Number of events..........: 131

cat("- Edit the column names...\n")

- Edit the column names...

colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")

- Correct the variable types...

COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.targets.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")

- Writing results to Excel-file...

head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.targets.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")

- Removing intermediate files...

rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.targets.COX.results)

Model 2

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.targets)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.targets.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.targets.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.targets.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.targets", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque target-of-interest and start survival analysis.
   > processing [SCGB3A2]; 1 out of 35 target-of-interest.
   > cross tabulation of SCGB3A2-stratum.

[2.37, 5.44) [5.44,10.32] 
         546          546 

   > fitting the model for SCGB3A2-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.44,10.32] -1.534e-01  8.578e-01  1.963e-01 -0.782 0.434493    
Age                                                            3.999e-02  1.041e+00  1.329e-02  3.008 0.002630 ** 
Gendermale                                                     4.433e-01  1.558e+00  2.378e-01  1.864 0.062262 .  
ORdate_year2002                                                9.274e-01  2.528e+00  1.101e+00  0.842 0.399688    
ORdate_year2003                                               -5.783e-02  9.438e-01  1.136e+00 -0.051 0.959379    
ORdate_year2004                                                3.036e-01  1.355e+00  1.089e+00  0.279 0.780520    
ORdate_year2005                                                8.248e-01  2.281e+00  1.062e+00  0.777 0.437311    
ORdate_year2006                                                8.832e-01  2.419e+00  1.049e+00  0.842 0.399807    
ORdate_year2007                                                3.816e-02  1.039e+00  1.089e+00  0.035 0.972047    
ORdate_year2008                                                8.750e-01  2.399e+00  1.057e+00  0.828 0.407925    
ORdate_year2009                                                1.534e-01  1.166e+00  1.076e+00  0.143 0.886623    
ORdate_year2010                                                4.120e-01  1.510e+00  1.073e+00  0.384 0.700907    
ORdate_year2011                                                5.837e-01  1.793e+00  1.090e+00  0.536 0.592166    
ORdate_year2012                                                1.005e+00  2.731e+00  1.060e+00  0.948 0.343163    
ORdate_year2013                                                8.733e-01  2.395e+00  1.067e+00  0.819 0.412963    
ORdate_year2014                                                5.869e-01  1.798e+00  1.092e+00  0.538 0.590914    
ORdate_year2015                                               -8.751e-02  9.162e-01  1.232e+00 -0.071 0.943388    
ORdate_year2016                                                4.153e-01  1.515e+00  1.234e+00  0.337 0.736389    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.250e-01  8.825e-01  3.301e-01 -0.379 0.704854    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.431e-01  1.902e+00  2.046e-01  3.143 0.001674 ** 
SmokerStatusEx-smoker                                         -4.897e-01  6.128e-01  2.195e-01 -2.231 0.025699 *  
SmokerStatusNever smoked                                      -7.427e-01  4.758e-01  3.231e-01 -2.299 0.021530 *  
Med.Statin.LLDno                                               4.027e-01  1.496e+00  2.267e-01  1.776 0.075664 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         5.980e-02  1.062e+00  2.803e-01  0.213 0.831067    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.097e-02  9.891e-01  5.039e-03 -2.177 0.029481 *  
BMI                                                            2.180e-02  1.022e+00  2.581e-02  0.845 0.398280    
MedHx_CVDyes                                                   8.053e-01  2.237e+00  2.364e-01  3.407 0.000657 ***
stenose0-49%                                                  -1.669e+01  5.668e-08  2.614e+03 -0.006 0.994907    
stenose50-70%                                                 -1.545e+00  2.133e-01  6.486e-01 -2.383 0.017191 *  
stenose70-90%                                                 -9.647e-01  3.811e-01  4.441e-01 -2.172 0.029851 *  
stenose90-99%                                                 -1.099e+00  3.333e-01  4.489e-01 -2.448 0.014369 *  
stenose100% (Occlusion)                                       -1.628e+01  8.499e-08  2.275e+03 -0.007 0.994290    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.707e+01  3.846e-08  3.754e+03 -0.005 0.996371    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.44,10.32] 8.578e-01  1.166e+00   0.58388    1.2602
Age                                                           1.041e+00  9.608e-01   1.01403    1.0683
Gendermale                                                    1.558e+00  6.419e-01   0.97754    2.4825
ORdate_year2002                                               2.528e+00  3.956e-01   0.29201   21.8862
ORdate_year2003                                               9.438e-01  1.060e+00   0.10194    8.7383
ORdate_year2004                                               1.355e+00  7.382e-01   0.16013   11.4608
ORdate_year2005                                               2.281e+00  4.383e-01   0.28468   18.2831
ORdate_year2006                                               2.419e+00  4.135e-01   0.30951   18.9003
ORdate_year2007                                               1.039e+00  9.626e-01   0.12292    8.7809
ORdate_year2008                                               2.399e+00  4.168e-01   0.30198   19.0583
ORdate_year2009                                               1.166e+00  8.578e-01   0.14159    9.5981
ORdate_year2010                                               1.510e+00  6.623e-01   0.18448   12.3560
ORdate_year2011                                               1.793e+00  5.578e-01   0.21187   15.1671
ORdate_year2012                                               2.731e+00  3.662e-01   0.34210   21.8030
ORdate_year2013                                               2.395e+00  4.176e-01   0.29599   19.3772
ORdate_year2014                                               1.798e+00  5.560e-01   0.21157   15.2877
ORdate_year2015                                               9.162e-01  1.091e+00   0.08185   10.2563
ORdate_year2016                                               1.515e+00  6.601e-01   0.13498   17.0008
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.825e-01  1.133e+00   0.46208    1.6853
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.902e+00  5.257e-01   1.27384    2.8411
SmokerStatusEx-smoker                                         6.128e-01  1.632e+00   0.39857    0.9423
SmokerStatusNever smoked                                      4.758e-01  2.102e+00   0.25257    0.8964
Med.Statin.LLDno                                              1.496e+00  6.685e-01   0.95925    2.3325
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.062e+00  9.420e-01   0.61288    1.8389
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.891e-01  1.011e+00   0.97937    0.9989
BMI                                                           1.022e+00  9.784e-01   0.97162    1.0751
MedHx_CVDyes                                                  2.237e+00  4.469e-01   1.40783    3.5560
stenose0-49%                                                  5.668e-08  1.764e+07   0.00000       Inf
stenose50-70%                                                 2.133e-01  4.689e+00   0.05982    0.7603
stenose70-90%                                                 3.811e-01  2.624e+00   0.15959    0.9101
stenose90-99%                                                 3.333e-01  3.001e+00   0.13825    0.8033
stenose100% (Occlusion)                                       8.499e-08  1.177e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.846e-08  2.600e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.749  (se = 0.021 )
Likelihood ratio test= 91.73  on 33 df,   p=2e-07
Wald test            = 76.77  on 33 df,   p=2e-05
Score (logrank) test = 98.85  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' SCGB3A2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: SCGB3A2 
Effect size...............: -0.153389 
Standard error............: 0.196268 
Odds ratio (effect size)..: 0.858 
Lower 95% CI..............: 0.584 
Upper 95% CI..............: 1.26 
T-value...................: -0.781526 
P-value...................: 0.4344934 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [LIX1]; 2 out of 35 target-of-interest.
   > cross tabulation of LIX1-stratum.

[5.45, 7.95) [7.95,10.73] 
         546          546 

   > fitting the model for LIX1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.95,10.73]  2.270e-01  1.255e+00  2.008e-01  1.131 0.258206    
Age                                                            4.040e-02  1.041e+00  1.328e-02  3.042 0.002347 ** 
Gendermale                                                     4.629e-01  1.589e+00  2.365e-01  1.957 0.050353 .  
ORdate_year2002                                                8.666e-01  2.379e+00  1.102e+00  0.786 0.431822    
ORdate_year2003                                               -1.366e-01  8.723e-01  1.139e+00 -0.120 0.904591    
ORdate_year2004                                                2.495e-01  1.283e+00  1.092e+00  0.228 0.819305    
ORdate_year2005                                                7.741e-01  2.169e+00  1.063e+00  0.728 0.466568    
ORdate_year2006                                                8.433e-01  2.324e+00  1.050e+00  0.803 0.421694    
ORdate_year2007                                                4.726e-03  1.005e+00  1.089e+00  0.004 0.996537    
ORdate_year2008                                                8.504e-01  2.341e+00  1.057e+00  0.804 0.421150    
ORdate_year2009                                                1.714e-01  1.187e+00  1.072e+00  0.160 0.872904    
ORdate_year2010                                                3.319e-01  1.394e+00  1.076e+00  0.308 0.757799    
ORdate_year2011                                                5.208e-01  1.683e+00  1.091e+00  0.477 0.633223    
ORdate_year2012                                                9.971e-01  2.710e+00  1.058e+00  0.942 0.346104    
ORdate_year2013                                                8.850e-01  2.423e+00  1.066e+00  0.830 0.406387    
ORdate_year2014                                                6.296e-01  1.877e+00  1.091e+00  0.577 0.563962    
ORdate_year2015                                               -6.822e-02  9.341e-01  1.232e+00 -0.055 0.955837    
ORdate_year2016                                                4.031e-01  1.496e+00  1.233e+00  0.327 0.743785    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.265e-01  8.812e-01  3.305e-01 -0.383 0.701918    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.301e-01  1.878e+00  2.056e-01  3.064 0.002181 ** 
SmokerStatusEx-smoker                                         -5.232e-01  5.926e-01  2.187e-01 -2.392 0.016744 *  
SmokerStatusNever smoked                                      -7.570e-01  4.691e-01  3.231e-01 -2.343 0.019125 *  
Med.Statin.LLDno                                               4.146e-01  1.514e+00  2.264e-01  1.831 0.067121 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         3.564e-02  1.036e+00  2.801e-01  0.127 0.898765    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.105e-02  9.890e-01  5.000e-03 -2.210 0.027131 *  
BMI                                                            2.210e-02  1.022e+00  2.594e-02  0.852 0.394280    
MedHx_CVDyes                                                   8.317e-01  2.297e+00  2.362e-01  3.521 0.000431 ***
stenose0-49%                                                  -1.767e+01  2.111e-08  4.017e+03 -0.004 0.996490    
stenose50-70%                                                 -1.516e+00  2.197e-01  6.500e-01 -2.332 0.019713 *  
stenose70-90%                                                 -9.406e-01  3.904e-01  4.457e-01 -2.110 0.034822 *  
stenose90-99%                                                 -1.073e+00  3.418e-01  4.514e-01 -2.378 0.017391 *  
stenose100% (Occlusion)                                       -1.732e+01  3.006e-08  3.612e+03 -0.005 0.996174    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.813e+01  1.340e-08  6.047e+03 -0.003 0.997608    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.95,10.73] 1.255e+00  7.969e-01   0.84661    1.8599
Age                                                           1.041e+00  9.604e-01   1.01448    1.0687
Gendermale                                                    1.589e+00  6.295e-01   0.99929    2.5256
ORdate_year2002                                               2.379e+00  4.204e-01   0.27415   20.6396
ORdate_year2003                                               8.723e-01  1.146e+00   0.09348    8.1400
ORdate_year2004                                               1.283e+00  7.792e-01   0.15095   10.9108
ORdate_year2005                                               2.169e+00  4.611e-01   0.26989   17.4250
ORdate_year2006                                               2.324e+00  4.303e-01   0.29709   18.1790
ORdate_year2007                                               1.005e+00  9.953e-01   0.11887    8.4924
ORdate_year2008                                               2.341e+00  4.272e-01   0.29475   18.5873
ORdate_year2009                                               1.187e+00  8.425e-01   0.14530    9.6970
ORdate_year2010                                               1.394e+00  7.175e-01   0.16902   11.4913
ORdate_year2011                                               1.683e+00  5.940e-01   0.19824   14.2950
ORdate_year2012                                               2.710e+00  3.690e-01   0.34058   21.5690
ORdate_year2013                                               2.423e+00  4.127e-01   0.29994   19.5737
ORdate_year2014                                               1.877e+00  5.328e-01   0.22111   15.9300
ORdate_year2015                                               9.341e-01  1.071e+00   0.08352   10.4465
ORdate_year2016                                               1.496e+00  6.682e-01   0.13342   16.7851
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.812e-01  1.135e+00   0.46106    1.6841
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.878e+00  5.326e-01   1.25493    2.8095
SmokerStatusEx-smoker                                         5.926e-01  1.687e+00   0.38601    0.9098
SmokerStatusNever smoked                                      4.691e-01  2.132e+00   0.24901    0.8836
Med.Statin.LLDno                                              1.514e+00  6.606e-01   0.97119    2.3594
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.036e+00  9.650e-01   0.59844    1.7945
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.890e-01  1.011e+00   0.97937    0.9988
BMI                                                           1.022e+00  9.781e-01   0.97166    1.0757
MedHx_CVDyes                                                  2.297e+00  4.353e-01   1.44582    3.6500
stenose0-49%                                                  2.111e-08  4.737e+07   0.00000       Inf
stenose50-70%                                                 2.197e-01  4.553e+00   0.06144    0.7853
stenose70-90%                                                 3.904e-01  2.562e+00   0.16298    0.9351
stenose90-99%                                                 3.418e-01  2.926e+00   0.14112    0.8279
stenose100% (Occlusion)                                       3.006e-08  3.327e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.340e-08  7.464e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.75  (se = 0.021 )
Likelihood ratio test= 92.4  on 33 df,   p=2e-07
Wald test            = 78.22  on 33 df,   p=2e-05
Score (logrank) test = 98.82  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LIX1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: LIX1 
Effect size...............: 0.227014 
Standard error............: 0.200783 
Odds ratio (effect size)..: 1.255 
Lower 95% CI..............: 0.847 
Upper 95% CI..............: 1.86 
T-value...................: 1.130641 
P-value...................: 0.2582064 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [IGSF9B]; 3 out of 35 target-of-interest.
   > cross tabulation of IGSF9B-stratum.

[2.37, 5.88) [5.88,11.00] 
         546          546 

   > fitting the model for IGSF9B-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.88,11.00] -3.491e-01  7.053e-01  2.022e-01 -1.727 0.084192 .  
Age                                                            3.963e-02  1.040e+00  1.325e-02  2.991 0.002782 ** 
Gendermale                                                     4.038e-01  1.498e+00  2.386e-01  1.693 0.090511 .  
ORdate_year2002                                                8.956e-01  2.449e+00  1.103e+00  0.812 0.416924    
ORdate_year2003                                               -1.243e-01  8.831e-01  1.137e+00 -0.109 0.912943    
ORdate_year2004                                                2.765e-01  1.318e+00  1.091e+00  0.253 0.799932    
ORdate_year2005                                                8.027e-01  2.232e+00  1.064e+00  0.754 0.450723    
ORdate_year2006                                                8.762e-01  2.402e+00  1.049e+00  0.835 0.403542    
ORdate_year2007                                                1.713e-02  1.017e+00  1.090e+00  0.016 0.987462    
ORdate_year2008                                                8.705e-01  2.388e+00  1.060e+00  0.821 0.411447    
ORdate_year2009                                                1.030e-01  1.108e+00  1.076e+00  0.096 0.923786    
ORdate_year2010                                                4.277e-01  1.534e+00  1.075e+00  0.398 0.690596    
ORdate_year2011                                                6.176e-01  1.854e+00  1.091e+00  0.566 0.571267    
ORdate_year2012                                                1.014e+00  2.756e+00  1.061e+00  0.956 0.339310    
ORdate_year2013                                                8.935e-01  2.444e+00  1.069e+00  0.836 0.403403    
ORdate_year2014                                                6.362e-01  1.889e+00  1.095e+00  0.581 0.561347    
ORdate_year2015                                               -4.890e-02  9.523e-01  1.235e+00 -0.040 0.968401    
ORdate_year2016                                                4.594e-01  1.583e+00  1.236e+00  0.372 0.710106    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.116e-01  8.944e-01  3.300e-01 -0.338 0.735185    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.550e-01  1.925e+00  2.049e-01  3.197 0.001390 ** 
SmokerStatusEx-smoker                                         -4.667e-01  6.270e-01  2.187e-01 -2.134 0.032820 *  
SmokerStatusNever smoked                                      -7.206e-01  4.865e-01  3.230e-01 -2.231 0.025679 *  
Med.Statin.LLDno                                               4.208e-01  1.523e+00  2.258e-01  1.863 0.062397 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         6.848e-02  1.071e+00  2.801e-01  0.244 0.806897    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.097e-02  9.891e-01  5.023e-03 -2.185 0.028897 *  
BMI                                                            1.954e-02  1.020e+00  2.589e-02  0.755 0.450355    
MedHx_CVDyes                                                   8.183e-01  2.267e+00  2.360e-01  3.468 0.000524 ***
stenose0-49%                                                  -1.675e+01  5.293e-08  2.629e+03 -0.006 0.994915    
stenose50-70%                                                 -1.669e+00  1.884e-01  6.481e-01 -2.575 0.010019 *  
stenose70-90%                                                 -1.064e+00  3.451e-01  4.434e-01 -2.400 0.016417 *  
stenose90-99%                                                 -1.199e+00  3.015e-01  4.494e-01 -2.668 0.007627 ** 
stenose100% (Occlusion)                                       -1.631e+01  8.253e-08  2.263e+03 -0.007 0.994249    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.706e+01  3.905e-08  3.735e+03 -0.005 0.996356    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.88,11.00] 7.053e-01  1.418e+00   0.47457    1.0483
Age                                                           1.040e+00  9.611e-01   1.01375    1.0678
Gendermale                                                    1.498e+00  6.678e-01   0.93823    2.3902
ORdate_year2002                                               2.449e+00  4.084e-01   0.28176   21.2809
ORdate_year2003                                               8.831e-01  1.132e+00   0.09516    8.1962
ORdate_year2004                                               1.318e+00  7.585e-01   0.15542   11.1845
ORdate_year2005                                               2.232e+00  4.481e-01   0.27711   17.9720
ORdate_year2006                                               2.402e+00  4.163e-01   0.30735   18.7694
ORdate_year2007                                               1.017e+00  9.830e-01   0.12014    8.6136
ORdate_year2008                                               2.388e+00  4.187e-01   0.29916   19.0644
ORdate_year2009                                               1.108e+00  9.022e-01   0.13447    9.1368
ORdate_year2010                                               1.534e+00  6.520e-01   0.18667   12.6018
ORdate_year2011                                               1.854e+00  5.392e-01   0.21865   15.7284
ORdate_year2012                                               2.756e+00  3.628e-01   0.34449   22.0499
ORdate_year2013                                               2.444e+00  4.092e-01   0.30048   19.8730
ORdate_year2014                                               1.889e+00  5.293e-01   0.22081   16.1635
ORdate_year2015                                               9.523e-01  1.050e+00   0.08471   10.7051
ORdate_year2016                                               1.583e+00  6.316e-01   0.14044   17.8470
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.944e-01  1.118e+00   0.46840    1.7078
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.925e+00  5.195e-01   1.28841    2.8764
SmokerStatusEx-smoker                                         6.270e-01  1.595e+00   0.40847    0.9626
SmokerStatusNever smoked                                      4.865e-01  2.056e+00   0.25831    0.9162
Med.Statin.LLDno                                              1.523e+00  6.565e-01   0.97844    2.3712
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.071e+00  9.338e-01   0.61841    1.8544
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.891e-01  1.011e+00   0.97940    0.9989
BMI                                                           1.020e+00  9.806e-01   0.96928    1.0728
MedHx_CVDyes                                                  2.267e+00  4.412e-01   1.42742    3.5995
stenose0-49%                                                  5.293e-08  1.889e+07   0.00000       Inf
stenose50-70%                                                 1.884e-01  5.307e+00   0.05291    0.6712
stenose70-90%                                                 3.451e-01  2.897e+00   0.14474    0.8229
stenose90-99%                                                 3.015e-01  3.317e+00   0.12496    0.7274
stenose100% (Occlusion)                                       8.253e-08  1.212e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.905e-08  2.561e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.752  (se = 0.021 )
Likelihood ratio test= 94.13  on 33 df,   p=9e-08
Wald test            = 78.23  on 33 df,   p=2e-05
Score (logrank) test = 100.4  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IGSF9B ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: IGSF9B 
Effect size...............: -0.349107 
Standard error............: 0.202163 
Odds ratio (effect size)..: 0.705 
Lower 95% CI..............: 0.475 
Upper 95% CI..............: 1.048 
T-value...................: -1.726863 
P-value...................: 0.08419229 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [ALB]; 4 out of 35 target-of-interest.
   > cross tabulation of ALB-stratum.

[2.37,6.18) [6.18,9.89] 
        546         546 

   > fitting the model for ALB-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.18,9.89] -2.025e-01  8.167e-01  1.933e-01 -1.048 0.294692    
Age                                                           3.891e-02  1.040e+00  1.328e-02  2.931 0.003384 ** 
Gendermale                                                    4.658e-01  1.593e+00  2.378e-01  1.959 0.050141 .  
ORdate_year2002                                               1.023e+00  2.782e+00  1.099e+00  0.931 0.352016    
ORdate_year2003                                               3.718e-02  1.038e+00  1.132e+00  0.033 0.973797    
ORdate_year2004                                               3.565e-01  1.428e+00  1.087e+00  0.328 0.742896    
ORdate_year2005                                               8.746e-01  2.398e+00  1.059e+00  0.826 0.408669    
ORdate_year2006                                               9.511e-01  2.589e+00  1.044e+00  0.911 0.362393    
ORdate_year2007                                               1.195e-01  1.127e+00  1.086e+00  0.110 0.912455    
ORdate_year2008                                               9.381e-01  2.555e+00  1.055e+00  0.889 0.374075    
ORdate_year2009                                               1.962e-01  1.217e+00  1.071e+00  0.183 0.854617    
ORdate_year2010                                               4.377e-01  1.549e+00  1.071e+00  0.409 0.682824    
ORdate_year2011                                               6.444e-01  1.905e+00  1.087e+00  0.593 0.553353    
ORdate_year2012                                               1.052e+00  2.862e+00  1.058e+00  0.994 0.320415    
ORdate_year2013                                               9.058e-01  2.474e+00  1.065e+00  0.850 0.395180    
ORdate_year2014                                               5.873e-01  1.799e+00  1.091e+00  0.538 0.590253    
ORdate_year2015                                              -6.171e-02  9.402e-01  1.232e+00 -0.050 0.960041    
ORdate_year2016                                               4.491e-01  1.567e+00  1.233e+00  0.364 0.715705    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.341e-01  8.745e-01  3.299e-01 -0.407 0.684362    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.631e-01  1.941e+00  2.051e-01  3.234 0.001222 ** 
SmokerStatusEx-smoker                                        -5.128e-01  5.988e-01  2.179e-01 -2.354 0.018594 *  
SmokerStatusNever smoked                                     -7.749e-01  4.607e-01  3.227e-01 -2.401 0.016342 *  
Med.Statin.LLDno                                              4.064e-01  1.501e+00  2.262e-01  1.796 0.072470 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        4.369e-02  1.045e+00  2.794e-01  0.156 0.875726    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.115e-02  9.889e-01  5.090e-03 -2.191 0.028462 *  
BMI                                                           2.122e-02  1.021e+00  2.570e-02  0.826 0.409060    
MedHx_CVDyes                                                  8.352e-01  2.305e+00  2.368e-01  3.526 0.000421 ***
stenose0-49%                                                 -1.674e+01  5.375e-08  2.576e+03 -0.006 0.994815    
stenose50-70%                                                -1.559e+00  2.103e-01  6.497e-01 -2.400 0.016395 *  
stenose70-90%                                                -9.607e-01  3.826e-01  4.462e-01 -2.153 0.031294 *  
stenose90-99%                                                -1.110e+00  3.296e-01  4.494e-01 -2.470 0.013516 *  
stenose100% (Occlusion)                                      -1.632e+01  8.145e-08  2.279e+03 -0.007 0.994285    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.713e+01  3.632e-08  3.767e+03 -0.005 0.996371    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.18,9.89] 8.167e-01  1.224e+00   0.55918    1.1928
Age                                                          1.040e+00  9.618e-01   1.01297    1.0671
Gendermale                                                   1.593e+00  6.276e-01   0.99971    2.5393
ORdate_year2002                                              2.782e+00  3.594e-01   0.32249   24.0041
ORdate_year2003                                              1.038e+00  9.635e-01   0.11286    9.5446
ORdate_year2004                                              1.428e+00  7.001e-01   0.16969   12.0236
ORdate_year2005                                              2.398e+00  4.170e-01   0.30116   19.0929
ORdate_year2006                                              2.589e+00  3.863e-01   0.33434   20.0435
ORdate_year2007                                              1.127e+00  8.874e-01   0.13398    9.4778
ORdate_year2008                                              2.555e+00  3.914e-01   0.32289   20.2193
ORdate_year2009                                              1.217e+00  8.219e-01   0.14925    9.9193
ORdate_year2010                                              1.549e+00  6.455e-01   0.18982   12.6424
ORdate_year2011                                              1.905e+00  5.250e-01   0.22621   16.0391
ORdate_year2012                                              2.862e+00  3.494e-01   0.35958   22.7859
ORdate_year2013                                              2.474e+00  4.042e-01   0.30659   19.9634
ORdate_year2014                                              1.799e+00  5.558e-01   0.21216   15.2567
ORdate_year2015                                              9.402e-01  1.064e+00   0.08410   10.5104
ORdate_year2016                                              1.567e+00  6.382e-01   0.13978   17.5643
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.745e-01  1.144e+00   0.45812    1.6693
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.941e+00  5.152e-01   1.29851    2.9010
SmokerStatusEx-smoker                                        5.988e-01  1.670e+00   0.39070    0.9178
SmokerStatusNever smoked                                     4.607e-01  2.170e+00   0.24476    0.8673
Med.Statin.LLDno                                             1.501e+00  6.661e-01   0.96362    2.3391
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.045e+00  9.572e-01   0.60418    1.8063
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.889e-01  1.011e+00   0.97909    0.9988
BMI                                                          1.021e+00  9.790e-01   0.97127    1.0742
MedHx_CVDyes                                                 2.305e+00  4.338e-01   1.44916    3.6669
stenose0-49%                                                 5.375e-08  1.860e+07   0.00000       Inf
stenose50-70%                                                2.103e-01  4.755e+00   0.05887    0.7514
stenose70-90%                                                3.826e-01  2.614e+00   0.15958    0.9173
stenose90-99%                                                3.296e-01  3.034e+00   0.13660    0.7952
stenose100% (Occlusion)                                      8.145e-08  1.228e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                3.632e-08  2.753e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.747  (se = 0.022 )
Likelihood ratio test= 92.21  on 33 df,   p=2e-07
Wald test            = 77.65  on 33 df,   p=2e-05
Score (logrank) test = 99.02  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' ALB ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: ALB 
Effect size...............: -0.20251 
Standard error............: 0.193256 
Odds ratio (effect size)..: 0.817 
Lower 95% CI..............: 0.559 
Upper 95% CI..............: 1.193 
T-value...................: -1.047883 
P-value...................: 0.2946924 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [OR10A4]; 5 out of 35 target-of-interest.
   > cross tabulation of OR10A4-stratum.

[2.37,5.66) [5.66,9.58] 
        546         546 

   > fitting the model for OR10A4-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.66,9.58] -2.714e-01  7.623e-01  1.951e-01 -1.391 0.164153    
Age                                                           3.795e-02  1.039e+00  1.337e-02  2.838 0.004540 ** 
Gendermale                                                    4.455e-01  1.561e+00  2.379e-01  1.873 0.061062 .  
ORdate_year2002                                               1.005e+00  2.732e+00  1.098e+00  0.915 0.360139    
ORdate_year2003                                               4.984e-02  1.051e+00  1.132e+00  0.044 0.964875    
ORdate_year2004                                               3.371e-01  1.401e+00  1.086e+00  0.310 0.756368    
ORdate_year2005                                               8.574e-01  2.357e+00  1.058e+00  0.810 0.417933    
ORdate_year2006                                               9.321e-01  2.540e+00  1.044e+00  0.893 0.371884    
ORdate_year2007                                               8.944e-02  1.094e+00  1.086e+00  0.082 0.934372    
ORdate_year2008                                               9.441e-01  2.570e+00  1.055e+00  0.895 0.370931    
ORdate_year2009                                               2.073e-01  1.230e+00  1.070e+00  0.194 0.846374    
ORdate_year2010                                               4.374e-01  1.549e+00  1.071e+00  0.409 0.682900    
ORdate_year2011                                               6.576e-01  1.930e+00  1.087e+00  0.605 0.545055    
ORdate_year2012                                               1.050e+00  2.857e+00  1.058e+00  0.992 0.320958    
ORdate_year2013                                               8.798e-01  2.410e+00  1.066e+00  0.825 0.409308    
ORdate_year2014                                               5.559e-01  1.743e+00  1.090e+00  0.510 0.610176    
ORdate_year2015                                              -8.470e-02  9.188e-01  1.231e+00 -0.069 0.945145    
ORdate_year2016                                               4.350e-01  1.545e+00  1.232e+00  0.353 0.724106    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.399e-01  8.694e-01  3.304e-01 -0.424 0.671918    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.618e-01  1.938e+00  2.048e-01  3.231 0.001233 ** 
SmokerStatusEx-smoker                                        -5.147e-01  5.977e-01  2.179e-01 -2.363 0.018148 *  
SmokerStatusNever smoked                                     -7.595e-01  4.679e-01  3.219e-01 -2.359 0.018301 *  
Med.Statin.LLDno                                              4.020e-01  1.495e+00  2.264e-01  1.775 0.075853 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        4.961e-02  1.051e+00  2.793e-01  0.178 0.859032    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.123e-02  9.888e-01  5.085e-03 -2.208 0.027270 *  
BMI                                                           2.179e-02  1.022e+00  2.567e-02  0.849 0.395909    
MedHx_CVDyes                                                  8.258e-01  2.284e+00  2.363e-01  3.495 0.000474 ***
stenose0-49%                                                 -1.671e+01  5.535e-08  2.527e+03 -0.007 0.994724    
stenose50-70%                                                -1.521e+00  2.185e-01  6.515e-01 -2.335 0.019566 *  
stenose70-90%                                                -9.231e-01  3.973e-01  4.504e-01 -2.049 0.040432 *  
stenose90-99%                                                -1.053e+00  3.487e-01  4.534e-01 -2.323 0.020157 *  
stenose100% (Occlusion)                                      -1.627e+01  8.576e-08  2.303e+03 -0.007 0.994363    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.708e+01  3.835e-08  3.781e+03 -0.005 0.996396    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.66,9.58] 7.623e-01  1.312e+00   0.52003    1.1174
Age                                                          1.039e+00  9.628e-01   1.01181    1.0663
Gendermale                                                   1.561e+00  6.405e-01   0.97954    2.4885
ORdate_year2002                                              2.732e+00  3.660e-01   0.31742   23.5135
ORdate_year2003                                              1.051e+00  9.514e-01   0.11435    9.6614
ORdate_year2004                                              1.401e+00  7.139e-01   0.16658   11.7804
ORdate_year2005                                              2.357e+00  4.243e-01   0.29607   18.7627
ORdate_year2006                                              2.540e+00  3.937e-01   0.32832   19.6464
ORdate_year2007                                              1.094e+00  9.144e-01   0.13011    9.1910
ORdate_year2008                                              2.570e+00  3.890e-01   0.32499   20.3296
ORdate_year2009                                              1.230e+00  8.128e-01   0.15112   10.0167
ORdate_year2010                                              1.549e+00  6.457e-01   0.18993   12.6270
ORdate_year2011                                              1.930e+00  5.181e-01   0.22942   16.2396
ORdate_year2012                                              2.857e+00  3.500e-01   0.35940   22.7125
ORdate_year2013                                              2.410e+00  4.149e-01   0.29819   19.4831
ORdate_year2014                                              1.743e+00  5.736e-01   0.20573   14.7754
ORdate_year2015                                              9.188e-01  1.088e+00   0.08230   10.2575
ORdate_year2016                                              1.545e+00  6.472e-01   0.13799   17.2992
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.694e-01  1.150e+00   0.45502    1.6613
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.938e+00  5.159e-01   1.29741    2.8958
SmokerStatusEx-smoker                                        5.977e-01  1.673e+00   0.38994    0.9160
SmokerStatusNever smoked                                     4.679e-01  2.137e+00   0.24896    0.8793
Med.Statin.LLDno                                             1.495e+00  6.690e-01   0.95904    2.3297
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.051e+00  9.516e-01   0.60786    1.8167
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.888e-01  1.011e+00   0.97903    0.9987
BMI                                                          1.022e+00  9.784e-01   0.97189    1.0748
MedHx_CVDyes                                                 2.284e+00  4.379e-01   1.43719    3.6287
stenose0-49%                                                 5.535e-08  1.807e+07   0.00000       Inf
stenose50-70%                                                2.185e-01  4.576e+00   0.06095    0.7835
stenose70-90%                                                3.973e-01  2.517e+00   0.16432    0.9606
stenose90-99%                                                3.487e-01  2.868e+00   0.14340    0.8481
stenose100% (Occlusion)                                      8.576e-08  1.166e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                3.835e-08  2.608e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.75  (se = 0.022 )
Likelihood ratio test= 93.06  on 33 df,   p=1e-07
Wald test            = 79.06  on 33 df,   p=1e-05
Score (logrank) test = 100.5  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' OR10A4 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: OR10A4 
Effect size...............: -0.271449 
Standard error............: 0.195114 
Odds ratio (effect size)..: 0.762 
Lower 95% CI..............: 0.52 
Upper 95% CI..............: 1.117 
T-value...................: -1.391238 
P-value...................: 0.1641534 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [RASEF]; 6 out of 35 target-of-interest.
   > cross tabulation of RASEF-stratum.

[2.37, 7.51) [7.51,10.20] 
         546          546 

   > fitting the model for RASEF-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.51,10.20] -2.061e-01  8.137e-01  1.968e-01 -1.047 0.294977    
Age                                                            3.979e-02  1.041e+00  1.328e-02  2.996 0.002733 ** 
Gendermale                                                     4.359e-01  1.546e+00  2.382e-01  1.830 0.067298 .  
ORdate_year2002                                                9.652e-01  2.625e+00  1.099e+00  0.878 0.380021    
ORdate_year2003                                               -8.747e-03  9.913e-01  1.132e+00 -0.008 0.993836    
ORdate_year2004                                                3.327e-01  1.395e+00  1.088e+00  0.306 0.759791    
ORdate_year2005                                                8.527e-01  2.346e+00  1.060e+00  0.805 0.421023    
ORdate_year2006                                                9.323e-01  2.540e+00  1.045e+00  0.892 0.372422    
ORdate_year2007                                                7.237e-02  1.075e+00  1.087e+00  0.067 0.946911    
ORdate_year2008                                                9.353e-01  2.548e+00  1.056e+00  0.886 0.375735    
ORdate_year2009                                                1.913e-01  1.211e+00  1.071e+00  0.179 0.858246    
ORdate_year2010                                                4.661e-01  1.594e+00  1.072e+00  0.435 0.663699    
ORdate_year2011                                                6.326e-01  1.883e+00  1.088e+00  0.582 0.560816    
ORdate_year2012                                                1.069e+00  2.912e+00  1.058e+00  1.010 0.312448    
ORdate_year2013                                                8.795e-01  2.410e+00  1.067e+00  0.824 0.409858    
ORdate_year2014                                                6.072e-01  1.835e+00  1.091e+00  0.557 0.577759    
ORdate_year2015                                               -3.965e-02  9.611e-01  1.232e+00 -0.032 0.974332    
ORdate_year2016                                                4.975e-01  1.645e+00  1.235e+00  0.403 0.687035    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.384e-01  8.708e-01  3.308e-01 -0.418 0.675750    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.650e-01  1.944e+00  2.051e-01  3.243 0.001184 ** 
SmokerStatusEx-smoker                                         -5.056e-01  6.031e-01  2.178e-01 -2.321 0.020281 *  
SmokerStatusNever smoked                                      -7.612e-01  4.671e-01  3.222e-01 -2.363 0.018151 *  
Med.Statin.LLDno                                               4.293e-01  1.536e+00  2.276e-01  1.886 0.059313 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         5.866e-02  1.060e+00  2.799e-01  0.210 0.833968    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.160e-02  9.885e-01  5.113e-03 -2.269 0.023291 *  
BMI                                                            2.252e-02  1.023e+00  2.574e-02  0.875 0.381773    
MedHx_CVDyes                                                   8.196e-01  2.270e+00  2.359e-01  3.474 0.000513 ***
stenose0-49%                                                  -1.778e+01  1.905e-08  4.181e+03 -0.004 0.996608    
stenose50-70%                                                 -1.576e+00  2.068e-01  6.483e-01 -2.431 0.015058 *  
stenose70-90%                                                 -1.002e+00  3.671e-01  4.457e-01 -2.249 0.024535 *  
stenose90-99%                                                 -1.139e+00  3.201e-01  4.498e-01 -2.532 0.011327 *  
stenose100% (Occlusion)                                       -1.731e+01  3.045e-08  3.693e+03 -0.005 0.996261    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.807e+01  1.417e-08  6.126e+03 -0.003 0.997646    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.51,10.20] 8.137e-01  1.229e+00   0.55327    1.1968
Age                                                           1.041e+00  9.610e-01   1.01386    1.0680
Gendermale                                                    1.546e+00  6.467e-01   0.96944    2.4664
ORdate_year2002                                               2.625e+00  3.809e-01   0.30430   22.6486
ORdate_year2003                                               9.913e-01  1.009e+00   0.10775    9.1199
ORdate_year2004                                               1.395e+00  7.170e-01   0.16532   11.7662
ORdate_year2005                                               2.346e+00  4.263e-01   0.29396   18.7228
ORdate_year2006                                               2.540e+00  3.936e-01   0.32748   19.7060
ORdate_year2007                                               1.075e+00  9.302e-01   0.12773    9.0484
ORdate_year2008                                               2.548e+00  3.925e-01   0.32166   20.1843
ORdate_year2009                                               1.211e+00  8.259e-01   0.14833    9.8850
ORdate_year2010                                               1.594e+00  6.275e-01   0.19499   13.0261
ORdate_year2011                                               1.883e+00  5.312e-01   0.22332   15.8688
ORdate_year2012                                               2.912e+00  3.435e-01   0.36606   23.1568
ORdate_year2013                                               2.410e+00  4.150e-01   0.29759   19.5107
ORdate_year2014                                               1.835e+00  5.449e-01   0.21641   15.5628
ORdate_year2015                                               9.611e-01  1.040e+00   0.08586   10.7592
ORdate_year2016                                               1.645e+00  6.080e-01   0.14618   18.5049
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.708e-01  1.148e+00   0.45530    1.6654
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.944e+00  5.143e-01   1.30089    2.9062
SmokerStatusEx-smoker                                         6.031e-01  1.658e+00   0.39354    0.9243
SmokerStatusNever smoked                                      4.671e-01  2.141e+00   0.24840    0.8784
Med.Statin.LLDno                                              1.536e+00  6.510e-01   0.98327    2.4000
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.060e+00  9.430e-01   0.61273    1.8352
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.885e-01  1.012e+00   0.97861    0.9984
BMI                                                           1.023e+00  9.777e-01   0.97245    1.0757
MedHx_CVDyes                                                  2.270e+00  4.406e-01   1.42929    3.6037
stenose0-49%                                                  1.905e-08  5.249e+07   0.00000       Inf
stenose50-70%                                                 2.068e-01  4.836e+00   0.05803    0.7368
stenose70-90%                                                 3.671e-01  2.724e+00   0.15323    0.8793
stenose90-99%                                                 3.201e-01  3.124e+00   0.13257    0.7730
stenose100% (Occlusion)                                       3.045e-08  3.284e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.417e-08  7.058e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.748  (se = 0.022 )
Likelihood ratio test= 92.21  on 33 df,   p=2e-07
Wald test            = 76.76  on 33 df,   p=2e-05
Score (logrank) test = 98.63  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' RASEF ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: RASEF 
Effect size...............: -0.206133 
Standard error............: 0.19683 
Odds ratio (effect size)..: 0.814 
Lower 95% CI..............: 0.553 
Upper 95% CI..............: 1.197 
T-value...................: -1.047265 
P-value...................: 0.2949772 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [NEDD4]; 7 out of 35 target-of-interest.
   > cross tabulation of NEDD4-stratum.

[5.05, 8.86) [8.86,11.49] 
         546          546 

   > fitting the model for NEDD4-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.86,11.49] -1.628e-01  8.498e-01  1.949e-01 -0.835 0.403499    
Age                                                            3.936e-02  1.040e+00  1.331e-02  2.958 0.003100 ** 
Gendermale                                                     4.601e-01  1.584e+00  2.376e-01  1.937 0.052784 .  
ORdate_year2002                                                1.050e+00  2.858e+00  1.103e+00  0.952 0.341055    
ORdate_year2003                                                5.357e-02  1.055e+00  1.133e+00  0.047 0.962303    
ORdate_year2004                                                3.782e-01  1.460e+00  1.088e+00  0.348 0.728086    
ORdate_year2005                                                8.947e-01  2.447e+00  1.059e+00  0.845 0.398093    
ORdate_year2006                                                9.647e-01  2.624e+00  1.045e+00  0.924 0.355743    
ORdate_year2007                                                1.133e-01  1.120e+00  1.087e+00  0.104 0.916991    
ORdate_year2008                                                9.597e-01  2.611e+00  1.056e+00  0.908 0.363692    
ORdate_year2009                                                2.336e-01  1.263e+00  1.071e+00  0.218 0.827287    
ORdate_year2010                                                4.867e-01  1.627e+00  1.073e+00  0.454 0.650013    
ORdate_year2011                                                6.539e-01  1.923e+00  1.087e+00  0.601 0.547651    
ORdate_year2012                                                1.085e+00  2.961e+00  1.059e+00  1.025 0.305425    
ORdate_year2013                                                9.183e-01  2.505e+00  1.066e+00  0.862 0.388893    
ORdate_year2014                                                6.320e-01  1.881e+00  1.091e+00  0.579 0.562411    
ORdate_year2015                                               -1.765e-02  9.825e-01  1.234e+00 -0.014 0.988581    
ORdate_year2016                                                5.101e-01  1.666e+00  1.237e+00  0.412 0.680006    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.060e-01  8.994e-01  3.305e-01 -0.321 0.748378    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.621e-01  1.939e+00  2.051e-01  3.228 0.001246 ** 
SmokerStatusEx-smoker                                         -5.034e-01  6.045e-01  2.178e-01 -2.312 0.020799 *  
SmokerStatusNever smoked                                      -7.626e-01  4.664e-01  3.225e-01 -2.365 0.018030 *  
Med.Statin.LLDno                                               4.088e-01  1.505e+00  2.267e-01  1.804 0.071284 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         4.653e-02  1.048e+00  2.797e-01  0.166 0.867875    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.129e-02  9.888e-01  5.098e-03 -2.214 0.026811 *  
BMI                                                            2.264e-02  1.023e+00  2.583e-02  0.876 0.380878    
MedHx_CVDyes                                                   8.251e-01  2.282e+00  2.362e-01  3.493 0.000478 ***
stenose0-49%                                                  -1.676e+01  5.237e-08  2.554e+03 -0.007 0.994762    
stenose50-70%                                                 -1.566e+00  2.088e-01  6.498e-01 -2.410 0.015939 *  
stenose70-90%                                                 -9.873e-01  3.726e-01  4.454e-01 -2.217 0.026638 *  
stenose90-99%                                                 -1.129e+00  3.233e-01  4.493e-01 -2.513 0.011957 *  
stenose100% (Occlusion)                                       -1.634e+01  8.035e-08  2.258e+03 -0.007 0.994227    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.715e+01  3.570e-08  3.740e+03 -0.005 0.996341    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.86,11.49] 8.498e-01  1.177e+00   0.58002    1.2450
Age                                                           1.040e+00  9.614e-01   1.01337    1.0676
Gendermale                                                    1.584e+00  6.312e-01   0.99448    2.5238
ORdate_year2002                                               2.858e+00  3.499e-01   0.32906   24.8162
ORdate_year2003                                               1.055e+00  9.478e-01   0.11442    9.7278
ORdate_year2004                                               1.460e+00  6.851e-01   0.17307   12.3120
ORdate_year2005                                               2.447e+00  4.087e-01   0.30713   19.4904
ORdate_year2006                                               2.624e+00  3.811e-01   0.33867   20.3314
ORdate_year2007                                               1.120e+00  8.929e-01   0.13307    9.4255
ORdate_year2008                                               2.611e+00  3.830e-01   0.32923   20.7037
ORdate_year2009                                               1.263e+00  7.917e-01   0.15493   10.2978
ORdate_year2010                                               1.627e+00  6.146e-01   0.19875   13.3184
ORdate_year2011                                               1.923e+00  5.200e-01   0.22820   16.2052
ORdate_year2012                                               2.961e+00  3.377e-01   0.37143   23.6017
ORdate_year2013                                               2.505e+00  3.992e-01   0.31019   20.2296
ORdate_year2014                                               1.881e+00  5.315e-01   0.22172   15.9636
ORdate_year2015                                               9.825e-01  1.018e+00   0.08757   11.0230
ORdate_year2016                                               1.666e+00  6.004e-01   0.14749   18.8073
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.994e-01  1.112e+00   0.47062    1.7189
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.939e+00  5.158e-01   1.29709    2.8983
SmokerStatusEx-smoker                                         6.045e-01  1.654e+00   0.39448    0.9263
SmokerStatusNever smoked                                      4.664e-01  2.144e+00   0.24793    0.8776
Med.Statin.LLDno                                              1.505e+00  6.644e-01   0.96519    2.3470
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.048e+00  9.545e-01   0.60551    1.8126
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.888e-01  1.011e+00   0.97894    0.9987
BMI                                                           1.023e+00  9.776e-01   0.97239    1.0760
MedHx_CVDyes                                                  2.282e+00  4.382e-01   1.43631    3.6258
stenose0-49%                                                  5.237e-08  1.909e+07   0.00000       Inf
stenose50-70%                                                 2.088e-01  4.789e+00   0.05843    0.7463
stenose70-90%                                                 3.726e-01  2.684e+00   0.15565    0.8919
stenose90-99%                                                 3.233e-01  3.093e+00   0.13401    0.7798
stenose100% (Occlusion)                                       8.035e-08  1.245e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.570e-08  2.801e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.748  (se = 0.022 )
Likelihood ratio test= 91.81  on 33 df,   p=2e-07
Wald test            = 76.74  on 33 df,   p=2e-05
Score (logrank) test = 98.57  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' NEDD4 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: NEDD4 
Effect size...............: -0.162782 
Standard error............: 0.194858 
Odds ratio (effect size)..: 0.85 
Lower 95% CI..............: 0.58 
Upper 95% CI..............: 1.245 
T-value...................: -0.835389 
P-value...................: 0.4034988 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [TCL1A]; 8 out of 35 target-of-interest.
   > cross tabulation of TCL1A-stratum.

[6.16, 8.59) [8.59,11.42] 
         546          546 

   > fitting the model for TCL1A-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.59,11.42]  3.725e-01  1.451e+00  2.040e-01  1.827 0.067752 .  
Age                                                            4.071e-02  1.042e+00  1.327e-02  3.068 0.002154 ** 
Gendermale                                                     4.559e-01  1.578e+00  2.360e-01  1.932 0.053368 .  
ORdate_year2002                                                7.866e-01  2.196e+00  1.103e+00  0.713 0.475809    
ORdate_year2003                                               -2.009e-01  8.180e-01  1.138e+00 -0.177 0.859893    
ORdate_year2004                                                1.899e-01  1.209e+00  1.091e+00  0.174 0.861897    
ORdate_year2005                                                7.046e-01  2.023e+00  1.063e+00  0.663 0.507454    
ORdate_year2006                                                7.775e-01  2.176e+00  1.049e+00  0.741 0.458631    
ORdate_year2007                                               -5.414e-02  9.473e-01  1.089e+00 -0.050 0.960350    
ORdate_year2008                                                8.024e-01  2.231e+00  1.057e+00  0.759 0.447836    
ORdate_year2009                                                1.201e-01  1.128e+00  1.072e+00  0.112 0.910737    
ORdate_year2010                                                2.629e-01  1.301e+00  1.076e+00  0.244 0.806965    
ORdate_year2011                                                4.762e-01  1.610e+00  1.090e+00  0.437 0.662112    
ORdate_year2012                                                9.868e-01  2.683e+00  1.057e+00  0.933 0.350683    
ORdate_year2013                                                8.850e-01  2.423e+00  1.065e+00  0.831 0.406178    
ORdate_year2014                                                6.429e-01  1.902e+00  1.091e+00  0.589 0.555609    
ORdate_year2015                                               -5.812e-02  9.435e-01  1.232e+00 -0.047 0.962375    
ORdate_year2016                                                3.824e-01  1.466e+00  1.233e+00  0.310 0.756489    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.164e-01  8.901e-01  3.306e-01 -0.352 0.724822    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.039e-01  1.829e+00  2.069e-01  2.919 0.003509 ** 
SmokerStatusEx-smoker                                         -5.206e-01  5.942e-01  2.193e-01 -2.374 0.017586 *  
SmokerStatusNever smoked                                      -7.527e-01  4.711e-01  3.230e-01 -2.330 0.019808 *  
Med.Statin.LLDno                                               3.959e-01  1.486e+00  2.262e-01  1.750 0.080054 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         5.024e-02  1.052e+00  2.803e-01  0.179 0.857737    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.117e-02  9.889e-01  4.995e-03 -2.236 0.025367 *  
BMI                                                            2.233e-02  1.023e+00  2.603e-02  0.858 0.390986    
MedHx_CVDyes                                                   8.288e-01  2.291e+00  2.357e-01  3.516 0.000439 ***
stenose0-49%                                                  -1.759e+01  2.294e-08  4.209e+03 -0.004 0.996665    
stenose50-70%                                                 -1.522e+00  2.183e-01  6.481e-01 -2.348 0.018869 *  
stenose70-90%                                                 -9.254e-01  3.964e-01  4.450e-01 -2.079 0.037579 *  
stenose90-99%                                                 -1.064e+00  3.450e-01  4.506e-01 -2.362 0.018178 *  
stenose100% (Occlusion)                                       -1.735e+01  2.929e-08  3.672e+03 -0.005 0.996231    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.815e+01  1.316e-08  6.031e+03 -0.003 0.997599    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.59,11.42] 1.451e+00  6.890e-01   0.97318    2.1647
Age                                                           1.042e+00  9.601e-01   1.01481    1.0690
Gendermale                                                    1.578e+00  6.339e-01   0.99341    2.5051
ORdate_year2002                                               2.196e+00  4.554e-01   0.25272   19.0805
ORdate_year2003                                               8.180e-01  1.222e+00   0.08793    7.6106
ORdate_year2004                                               1.209e+00  8.271e-01   0.14236   10.2694
ORdate_year2005                                               2.023e+00  4.943e-01   0.25185   16.2504
ORdate_year2006                                               2.176e+00  4.596e-01   0.27840   17.0083
ORdate_year2007                                               9.473e-01  1.056e+00   0.11209    8.0061
ORdate_year2008                                               2.231e+00  4.483e-01   0.28096   17.7128
ORdate_year2009                                               1.128e+00  8.868e-01   0.13802    9.2136
ORdate_year2010                                               1.301e+00  7.688e-01   0.15786   10.7178
ORdate_year2011                                               1.610e+00  6.212e-01   0.19024   13.6237
ORdate_year2012                                               2.683e+00  3.728e-01   0.33770   21.3099
ORdate_year2013                                               2.423e+00  4.127e-01   0.30022   19.5547
ORdate_year2014                                               1.902e+00  5.258e-01   0.22423   16.1334
ORdate_year2015                                               9.435e-01  1.060e+00   0.08436   10.5534
ORdate_year2016                                               1.466e+00  6.822e-01   0.13071   16.4392
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.901e-01  1.123e+00   0.46567    1.7015
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.829e+00  5.467e-01   1.21949    2.7438
SmokerStatusEx-smoker                                         5.942e-01  1.683e+00   0.38659    0.9132
SmokerStatusNever smoked                                      4.711e-01  2.123e+00   0.25012    0.8873
Med.Statin.LLDno                                              1.486e+00  6.731e-01   0.95371    2.3143
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.052e+00  9.510e-01   0.60707    1.8214
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.889e-01  1.011e+00   0.97926    0.9986
BMI                                                           1.023e+00  9.779e-01   0.97171    1.0761
MedHx_CVDyes                                                  2.291e+00  4.366e-01   1.44302    3.6358
stenose0-49%                                                  2.294e-08  4.360e+07   0.00000       Inf
stenose50-70%                                                 2.183e-01  4.581e+00   0.06129    0.7776
stenose70-90%                                                 3.964e-01  2.523e+00   0.16570    0.9482
stenose90-99%                                                 3.450e-01  2.899e+00   0.14266    0.8343
stenose100% (Occlusion)                                       2.929e-08  3.414e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.316e-08  7.599e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.753  (se = 0.02 )
Likelihood ratio test= 94.5  on 33 df,   p=8e-08
Wald test            = 81.09  on 33 df,   p=6e-06
Score (logrank) test = 101.5  on 33 df,   p=7e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' TCL1A ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: TCL1A 
Effect size...............: 0.372548 
Standard error............: 0.203951 
Odds ratio (effect size)..: 1.451 
Lower 95% CI..............: 0.973 
Upper 95% CI..............: 2.165 
T-value...................: 1.826656 
P-value...................: 0.06775154 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [FBXO15]; 9 out of 35 target-of-interest.
   > cross tabulation of FBXO15-stratum.

[5.12, 8.78) [8.78,11.78] 
         546          546 

   > fitting the model for FBXO15-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.78,11.78] -1.810e-01  8.344e-01  1.925e-01 -0.940 0.347106    
Age                                                            3.908e-02  1.040e+00  1.331e-02  2.937 0.003317 ** 
Gendermale                                                     4.657e-01  1.593e+00  2.379e-01  1.958 0.050282 .  
ORdate_year2002                                                1.039e+00  2.827e+00  1.101e+00  0.944 0.345260    
ORdate_year2003                                                6.317e-02  1.065e+00  1.133e+00  0.056 0.955555    
ORdate_year2004                                                3.816e-01  1.465e+00  1.088e+00  0.351 0.725687    
ORdate_year2005                                                9.058e-01  2.474e+00  1.059e+00  0.856 0.392214    
ORdate_year2006                                                9.663e-01  2.628e+00  1.045e+00  0.925 0.354965    
ORdate_year2007                                                1.374e-01  1.147e+00  1.087e+00  0.126 0.899476    
ORdate_year2008                                                9.473e-01  2.579e+00  1.056e+00  0.897 0.369516    
ORdate_year2009                                                2.415e-01  1.273e+00  1.070e+00  0.226 0.821521    
ORdate_year2010                                                4.628e-01  1.588e+00  1.072e+00  0.432 0.665863    
ORdate_year2011                                                6.762e-01  1.966e+00  1.088e+00  0.621 0.534342    
ORdate_year2012                                                1.077e+00  2.935e+00  1.059e+00  1.017 0.309181    
ORdate_year2013                                                9.250e-01  2.522e+00  1.066e+00  0.868 0.385404    
ORdate_year2014                                                6.287e-01  1.875e+00  1.091e+00  0.576 0.564374    
ORdate_year2015                                               -5.602e-02  9.455e-01  1.232e+00 -0.045 0.963718    
ORdate_year2016                                                4.989e-01  1.647e+00  1.235e+00  0.404 0.686260    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.220e-01  8.851e-01  3.300e-01 -0.370 0.711565    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.689e-01  1.952e+00  2.057e-01  3.252 0.001147 ** 
SmokerStatusEx-smoker                                         -5.146e-01  5.977e-01  2.178e-01 -2.362 0.018155 *  
SmokerStatusNever smoked                                      -7.642e-01  4.657e-01  3.226e-01 -2.369 0.017832 *  
Med.Statin.LLDno                                               4.050e-01  1.499e+00  2.267e-01  1.786 0.074024 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         3.592e-02  1.037e+00  2.800e-01  0.128 0.897933    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.127e-02  9.888e-01  5.100e-03 -2.211 0.027047 *  
BMI                                                            2.248e-02  1.023e+00  2.577e-02  0.872 0.383032    
MedHx_CVDyes                                                   8.254e-01  2.283e+00  2.361e-01  3.496 0.000472 ***
stenose0-49%                                                  -1.680e+01  5.066e-08  2.562e+03 -0.007 0.994768    
stenose50-70%                                                 -1.592e+00  2.034e-01  6.501e-01 -2.450 0.014303 *  
stenose70-90%                                                 -9.958e-01  3.694e-01  4.457e-01 -2.234 0.025483 *  
stenose90-99%                                                 -1.130e+00  3.232e-01  4.496e-01 -2.512 0.012003 *  
stenose100% (Occlusion)                                       -1.635e+01  7.924e-08  2.269e+03 -0.007 0.994250    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.715e+01  3.553e-08  3.748e+03 -0.005 0.996348    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.78,11.78] 8.344e-01  1.198e+00   0.57214    1.2169
Age                                                           1.040e+00  9.617e-01   1.01308    1.0673
Gendermale                                                    1.593e+00  6.277e-01   0.99943    2.5397
ORdate_year2002                                               2.827e+00  3.537e-01   0.32664   24.4686
ORdate_year2003                                               1.065e+00  9.388e-01   0.11553    9.8213
ORdate_year2004                                               1.465e+00  6.828e-01   0.17375   12.3464
ORdate_year2005                                               2.474e+00  4.042e-01   0.31062   19.7045
ORdate_year2006                                               2.628e+00  3.805e-01   0.33919   20.3650
ORdate_year2007                                               1.147e+00  8.717e-01   0.13617    9.6659
ORdate_year2008                                               2.579e+00  3.878e-01   0.32574   20.4130
ORdate_year2009                                               1.273e+00  7.855e-01   0.15621   10.3762
ORdate_year2010                                               1.588e+00  6.295e-01   0.19444   12.9768
ORdate_year2011                                               1.966e+00  5.085e-01   0.23301   16.5946
ORdate_year2012                                               2.935e+00  3.408e-01   0.36850   23.3705
ORdate_year2013                                               2.522e+00  3.965e-01   0.31231   20.3656
ORdate_year2014                                               1.875e+00  5.333e-01   0.22109   15.9030
ORdate_year2015                                               9.455e-01  1.058e+00   0.08459   10.5684
ORdate_year2016                                               1.647e+00  6.072e-01   0.14633   18.5361
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.851e-01  1.130e+00   0.46361    1.6900
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.952e+00  5.122e-01   1.30440    2.9216
SmokerStatusEx-smoker                                         5.977e-01  1.673e+00   0.39003    0.9161
SmokerStatusNever smoked                                      4.657e-01  2.147e+00   0.24746    0.8764
Med.Statin.LLDno                                              1.499e+00  6.670e-01   0.96143    2.3380
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.037e+00  9.647e-01   0.59875    1.7945
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.888e-01  1.011e+00   0.97896    0.9987
BMI                                                           1.023e+00  9.778e-01   0.97236    1.0757
MedHx_CVDyes                                                  2.283e+00  4.381e-01   1.43721    3.6259
stenose0-49%                                                  5.066e-08  1.974e+07   0.00000       Inf
stenose50-70%                                                 2.034e-01  4.916e+00   0.05689    0.7274
stenose70-90%                                                 3.694e-01  2.707e+00   0.15421    0.8850
stenose90-99%                                                 3.232e-01  3.094e+00   0.13388    0.7802
stenose100% (Occlusion)                                       7.924e-08  1.262e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.553e-08  2.815e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.748  (se = 0.022 )
Likelihood ratio test= 92  on 33 df,   p=2e-07
Wald test            = 76.95  on 33 df,   p=2e-05
Score (logrank) test = 98.75  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' FBXO15 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: FBXO15 
Effect size...............: -0.181018 
Standard error............: 0.192528 
Odds ratio (effect size)..: 0.834 
Lower 95% CI..............: 0.572 
Upper 95% CI..............: 1.217 
T-value...................: -0.940217 
P-value...................: 0.3471062 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [F5]; 10 out of 35 target-of-interest.
   > cross tabulation of F5-stratum.

[2.37,6.33) [6.33,8.71] 
        546         546 

   > fitting the model for F5-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.33,8.71] -2.929e-01  7.461e-01  1.949e-01 -1.503 0.132807    
Age                                                           3.870e-02  1.039e+00  1.331e-02  2.907 0.003644 ** 
Gendermale                                                    4.627e-01  1.588e+00  2.380e-01  1.944 0.051860 .  
ORdate_year2002                                               1.110e+00  3.035e+00  1.103e+00  1.006 0.314185    
ORdate_year2003                                               1.056e-01  1.111e+00  1.134e+00  0.093 0.925781    
ORdate_year2004                                               4.078e-01  1.503e+00  1.088e+00  0.375 0.707780    
ORdate_year2005                                               9.197e-01  2.508e+00  1.059e+00  0.869 0.385037    
ORdate_year2006                                               9.868e-01  2.683e+00  1.045e+00  0.944 0.344953    
ORdate_year2007                                               1.405e-01  1.151e+00  1.087e+00  0.129 0.897149    
ORdate_year2008                                               1.013e+00  2.753e+00  1.057e+00  0.958 0.338249    
ORdate_year2009                                               2.484e-01  1.282e+00  1.071e+00  0.232 0.816504    
ORdate_year2010                                               5.193e-01  1.681e+00  1.073e+00  0.484 0.628269    
ORdate_year2011                                               6.765e-01  1.967e+00  1.088e+00  0.622 0.533957    
ORdate_year2012                                               1.112e+00  3.041e+00  1.059e+00  1.050 0.293798    
ORdate_year2013                                               9.620e-01  2.617e+00  1.066e+00  0.902 0.366851    
ORdate_year2014                                               6.929e-01  1.999e+00  1.091e+00  0.635 0.525555    
ORdate_year2015                                               2.746e-02  1.028e+00  1.233e+00  0.022 0.982233    
ORdate_year2016                                               5.432e-01  1.721e+00  1.236e+00  0.440 0.660233    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.043e-01  9.009e-01  3.303e-01 -0.316 0.752141    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.726e-01  1.959e+00  2.050e-01  3.281 0.001034 ** 
SmokerStatusEx-smoker                                        -4.810e-01  6.181e-01  2.185e-01 -2.201 0.027703 *  
SmokerStatusNever smoked                                     -7.513e-01  4.718e-01  3.225e-01 -2.329 0.019841 *  
Med.Statin.LLDno                                              4.022e-01  1.495e+00  2.270e-01  1.772 0.076391 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        7.999e-02  1.083e+00  2.809e-01  0.285 0.775804    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.167e-02  9.884e-01  5.095e-03 -2.290 0.022019 *  
BMI                                                           2.253e-02  1.023e+00  2.572e-02  0.876 0.381149    
MedHx_CVDyes                                                  8.277e-01  2.288e+00  2.361e-01  3.505 0.000456 ***
stenose0-49%                                                 -1.680e+01  5.037e-08  2.541e+03 -0.007 0.994724    
stenose50-70%                                                -1.567e+00  2.087e-01  6.504e-01 -2.410 0.015973 *  
stenose70-90%                                                -9.675e-01  3.800e-01  4.462e-01 -2.168 0.030125 *  
stenose90-99%                                                -1.108e+00  3.302e-01  4.501e-01 -2.462 0.013829 *  
stenose100% (Occlusion)                                      -1.630e+01  8.315e-08  2.267e+03 -0.007 0.994263    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.707e+01  3.853e-08  3.735e+03 -0.005 0.996353    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.33,8.71] 7.461e-01  1.340e+00   0.50919    1.0931
Age                                                          1.039e+00  9.620e-01   1.01269    1.0669
Gendermale                                                   1.588e+00  6.296e-01   0.99628    2.5322
ORdate_year2002                                              3.035e+00  3.295e-01   0.34935   26.3627
ORdate_year2003                                              1.111e+00  8.998e-01   0.12045   10.2550
ORdate_year2004                                              1.503e+00  6.651e-01   0.17827   12.6799
ORdate_year2005                                              2.508e+00  3.986e-01   0.31492   19.9811
ORdate_year2006                                              2.683e+00  3.728e-01   0.34609   20.7924
ORdate_year2007                                              1.151e+00  8.689e-01   0.13666    9.6921
ORdate_year2008                                              2.753e+00  3.633e-01   0.34653   21.8637
ORdate_year2009                                              1.282e+00  7.800e-01   0.15727   10.4503
ORdate_year2010                                              1.681e+00  5.949e-01   0.20535   13.7594
ORdate_year2011                                              1.967e+00  5.084e-01   0.23333   16.5815
ORdate_year2012                                              3.041e+00  3.289e-01   0.38134   24.2433
ORdate_year2013                                              2.617e+00  3.821e-01   0.32386   21.1459
ORdate_year2014                                              1.999e+00  5.001e-01   0.23543   16.9803
ORdate_year2015                                              1.028e+00  9.729e-01   0.09171   11.5193
ORdate_year2016                                              1.721e+00  5.809e-01   0.15281   19.3923
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     9.009e-01  1.110e+00   0.47157    1.7213
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.959e+00  5.104e-01   1.31103    2.9281
SmokerStatusEx-smoker                                        6.181e-01  1.618e+00   0.40280    0.9486
SmokerStatusNever smoked                                     4.718e-01  2.120e+00   0.25071    0.8877
Med.Statin.LLDno                                             1.495e+00  6.688e-01   0.95824    2.3329
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.083e+00  9.231e-01   0.62469    1.8785
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.884e-01  1.012e+00   0.97858    0.9983
BMI                                                          1.023e+00  9.777e-01   0.97250    1.0757
MedHx_CVDyes                                                 2.288e+00  4.371e-01   1.44039    3.6346
stenose0-49%                                                 5.037e-08  1.985e+07   0.00000       Inf
stenose50-70%                                                2.087e-01  4.793e+00   0.05832    0.7465
stenose70-90%                                                3.800e-01  2.631e+00   0.15851    0.9112
stenose90-99%                                                3.302e-01  3.028e+00   0.13667    0.7979
stenose100% (Occlusion)                                      8.315e-08  1.203e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                3.853e-08  2.595e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.749  (se = 0.022 )
Likelihood ratio test= 93.39  on 33 df,   p=1e-07
Wald test            = 77.76  on 33 df,   p=2e-05
Score (logrank) test = 99.85  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' F5 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: F5 
Effect size...............: -0.292949 
Standard error............: 0.194893 
Odds ratio (effect size)..: 0.746 
Lower 95% CI..............: 0.509 
Upper 95% CI..............: 1.093 
T-value...................: -1.503125 
P-value...................: 0.1328067 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [TMEM212]; 11 out of 35 target-of-interest.
   > cross tabulation of TMEM212-stratum.

[4.88, 8.16) [8.16,10.71] 
         546          546 

   > fitting the model for TMEM212-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.16,10.71] -4.605e-01  6.309e-01  1.970e-01 -2.337 0.019422 *  
Age                                                            3.943e-02  1.040e+00  1.328e-02  2.969 0.002989 ** 
Gendermale                                                     4.692e-01  1.599e+00  2.393e-01  1.961 0.049927 *  
ORdate_year2002                                                1.205e+00  3.337e+00  1.103e+00  1.093 0.274537    
ORdate_year2003                                                1.567e-01  1.170e+00  1.133e+00  0.138 0.890036    
ORdate_year2004                                                4.123e-01  1.510e+00  1.086e+00  0.379 0.704334    
ORdate_year2005                                                9.583e-01  2.607e+00  1.058e+00  0.906 0.365181    
ORdate_year2006                                                9.903e-01  2.692e+00  1.043e+00  0.949 0.342593    
ORdate_year2007                                                2.245e-01  1.252e+00  1.087e+00  0.207 0.836381    
ORdate_year2008                                                1.065e+00  2.900e+00  1.057e+00  1.007 0.313915    
ORdate_year2009                                                2.311e-01  1.260e+00  1.070e+00  0.216 0.828999    
ORdate_year2010                                                5.207e-01  1.683e+00  1.072e+00  0.486 0.627062    
ORdate_year2011                                                7.814e-01  2.185e+00  1.089e+00  0.718 0.472830    
ORdate_year2012                                                1.112e+00  3.039e+00  1.059e+00  1.050 0.293804    
ORdate_year2013                                                9.270e-01  2.527e+00  1.065e+00  0.871 0.383895    
ORdate_year2014                                                6.724e-01  1.959e+00  1.090e+00  0.617 0.537153    
ORdate_year2015                                                1.444e-02  1.015e+00  1.232e+00  0.012 0.990648    
ORdate_year2016                                                5.625e-01  1.755e+00  1.234e+00  0.456 0.648487    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.102e-01  8.956e-01  3.299e-01 -0.334 0.738328    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.988e-01  2.011e+00  2.059e-01  3.394 0.000688 ***
SmokerStatusEx-smoker                                         -4.913e-01  6.118e-01  2.180e-01 -2.254 0.024224 *  
SmokerStatusNever smoked                                      -7.999e-01  4.494e-01  3.235e-01 -2.473 0.013406 *  
Med.Statin.LLDno                                               3.868e-01  1.472e+00  2.267e-01  1.706 0.087937 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         3.718e-02  1.038e+00  2.801e-01  0.133 0.894402    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.167e-02  9.884e-01  5.152e-03 -2.265 0.023508 *  
BMI                                                            2.329e-02  1.024e+00  2.568e-02  0.907 0.364433    
MedHx_CVDyes                                                   8.447e-01  2.327e+00  2.368e-01  3.567 0.000361 ***
stenose0-49%                                                  -1.696e+01  4.289e-08  2.578e+03 -0.007 0.994749    
stenose50-70%                                                 -1.632e+00  1.955e-01  6.520e-01 -2.503 0.012315 *  
stenose70-90%                                                 -1.024e+00  3.591e-01  4.477e-01 -2.288 0.022156 *  
stenose90-99%                                                 -1.162e+00  3.129e-01  4.519e-01 -2.571 0.010148 *  
stenose100% (Occlusion)                                       -1.637e+01  7.741e-08  2.309e+03 -0.007 0.994342    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.711e+01  3.724e-08  3.766e+03 -0.005 0.996376    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.16,10.71] 6.309e-01  1.585e+00   0.42882    0.9283
Age                                                           1.040e+00  9.613e-01   1.01349    1.0677
Gendermale                                                    1.599e+00  6.255e-01   1.00015    2.5556
ORdate_year2002                                               3.337e+00  2.997e-01   0.38426   28.9744
ORdate_year2003                                               1.170e+00  8.550e-01   0.12688   10.7825
ORdate_year2004                                               1.510e+00  6.621e-01   0.17958   12.7009
ORdate_year2005                                               2.607e+00  3.835e-01   0.32763   20.7480
ORdate_year2006                                               2.692e+00  3.715e-01   0.34823   20.8125
ORdate_year2007                                               1.252e+00  7.989e-01   0.14867   10.5381
ORdate_year2008                                               2.900e+00  3.449e-01   0.36519   23.0224
ORdate_year2009                                               1.260e+00  7.937e-01   0.15480   10.2549
ORdate_year2010                                               1.683e+00  5.941e-01   0.20603   13.7511
ORdate_year2011                                               2.185e+00  4.577e-01   0.25871   18.4474
ORdate_year2012                                               3.039e+00  3.291e-01   0.38150   24.2075
ORdate_year2013                                               2.527e+00  3.958e-01   0.31363   20.3580
ORdate_year2014                                               1.959e+00  5.105e-01   0.23149   16.5781
ORdate_year2015                                               1.015e+00  9.857e-01   0.09066   11.3531
ORdate_year2016                                               1.755e+00  5.698e-01   0.15630   19.7073
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.956e-01  1.117e+00   0.46913    1.7099
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        2.011e+00  4.972e-01   1.34351    3.0109
SmokerStatusEx-smoker                                         6.118e-01  1.634e+00   0.39907    0.9380
SmokerStatusNever smoked                                      4.494e-01  2.225e+00   0.23838    0.8471
Med.Statin.LLDno                                              1.472e+00  6.792e-01   0.94414    2.2958
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.038e+00  9.635e-01   0.59940    1.7971
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.884e-01  1.012e+00   0.97847    0.9984
BMI                                                           1.024e+00  9.770e-01   0.97332    1.0764
MedHx_CVDyes                                                  2.327e+00  4.297e-01   1.46310    3.7019
stenose0-49%                                                  4.289e-08  2.331e+07   0.00000       Inf
stenose50-70%                                                 1.955e-01  5.114e+00   0.05448    0.7018
stenose70-90%                                                 3.591e-01  2.785e+00   0.14934    0.8635
stenose90-99%                                                 3.129e-01  3.196e+00   0.12905    0.7588
stenose100% (Occlusion)                                       7.741e-08  1.292e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.724e-08  2.685e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.75  (se = 0.022 )
Likelihood ratio test= 96.64  on 33 df,   p=4e-08
Wald test            = 81.06  on 33 df,   p=6e-06
Score (logrank) test = 103  on 33 df,   p=4e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' TMEM212 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: TMEM212 
Effect size...............: -0.460542 
Standard error............: 0.197037 
Odds ratio (effect size)..: 0.631 
Lower 95% CI..............: 0.429 
Upper 95% CI..............: 0.928 
T-value...................: -2.337339 
P-value...................: 0.01942156 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [PTPRD]; 12 out of 35 target-of-interest.
   > cross tabulation of PTPRD-stratum.

[2.37,4.61) [4.61,6.22] 
        546         546 

   > fitting the model for PTPRD-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.61,6.22] -1.947e-01  8.230e-01  1.964e-01 -0.992 0.321435    
Age                                                           3.925e-02  1.040e+00  1.333e-02  2.944 0.003236 ** 
Gendermale                                                    4.624e-01  1.588e+00  2.371e-01  1.951 0.051109 .  
ORdate_year2002                                               9.657e-01  2.627e+00  1.099e+00  0.878 0.379723    
ORdate_year2003                                               2.817e-02  1.029e+00  1.132e+00  0.025 0.980146    
ORdate_year2004                                               3.128e-01  1.367e+00  1.088e+00  0.287 0.773792    
ORdate_year2005                                               8.280e-01  2.289e+00  1.060e+00  0.781 0.434788    
ORdate_year2006                                               9.093e-01  2.482e+00  1.045e+00  0.870 0.384420    
ORdate_year2007                                               7.662e-02  1.080e+00  1.086e+00  0.071 0.943775    
ORdate_year2008                                               9.073e-01  2.478e+00  1.055e+00  0.860 0.389869    
ORdate_year2009                                               2.078e-01  1.231e+00  1.070e+00  0.194 0.846089    
ORdate_year2010                                               4.126e-01  1.511e+00  1.071e+00  0.385 0.700142    
ORdate_year2011                                               6.446e-01  1.905e+00  1.087e+00  0.593 0.553227    
ORdate_year2012                                               1.013e+00  2.754e+00  1.058e+00  0.957 0.338336    
ORdate_year2013                                               8.868e-01  2.427e+00  1.065e+00  0.832 0.405216    
ORdate_year2014                                               6.375e-01  1.892e+00  1.092e+00  0.584 0.559235    
ORdate_year2015                                              -1.054e-01  9.000e-01  1.232e+00 -0.086 0.931818    
ORdate_year2016                                               4.648e-01  1.592e+00  1.233e+00  0.377 0.706309    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.192e-01  8.876e-01  3.302e-01 -0.361 0.718067    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.544e-01  1.924e+00  2.045e-01  3.199 0.001377 ** 
SmokerStatusEx-smoker                                        -5.007e-01  6.061e-01  2.184e-01 -2.293 0.021860 *  
SmokerStatusNever smoked                                     -7.687e-01  4.636e-01  3.236e-01 -2.376 0.017511 *  
Med.Statin.LLDno                                              3.952e-01  1.485e+00  2.273e-01  1.739 0.082022 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        4.003e-02  1.041e+00  2.794e-01  0.143 0.886103    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.114e-02  9.889e-01  5.074e-03 -2.195 0.028158 *  
BMI                                                           2.232e-02  1.023e+00  2.569e-02  0.869 0.385005    
MedHx_CVDyes                                                  8.049e-01  2.236e+00  2.360e-01  3.411 0.000648 ***
stenose0-49%                                                 -1.673e+01  5.406e-08  2.525e+03 -0.007 0.994712    
stenose50-70%                                                -1.546e+00  2.132e-01  6.470e-01 -2.389 0.016905 *  
stenose70-90%                                                -9.749e-01  3.772e-01  4.430e-01 -2.201 0.027760 *  
stenose90-99%                                                -1.108e+00  3.302e-01  4.477e-01 -2.476 0.013302 *  
stenose100% (Occlusion)                                      -1.632e+01  8.204e-08  2.279e+03 -0.007 0.994288    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.712e+01  3.663e-08  3.795e+03 -0.005 0.996400    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.61,6.22] 8.230e-01  1.215e+00   0.56006    1.2095
Age                                                          1.040e+00  9.615e-01   1.01321    1.0676
Gendermale                                                   1.588e+00  6.297e-01   0.99777    2.5272
ORdate_year2002                                              2.627e+00  3.807e-01   0.30450   22.6591
ORdate_year2003                                              1.029e+00  9.722e-01   0.11190    9.4543
ORdate_year2004                                              1.367e+00  7.314e-01   0.16203   11.5359
ORdate_year2005                                              2.289e+00  4.369e-01   0.28656   18.2788
ORdate_year2006                                              2.482e+00  4.028e-01   0.31993   19.2627
ORdate_year2007                                              1.080e+00  9.262e-01   0.12838    9.0791
ORdate_year2008                                              2.478e+00  4.036e-01   0.31324   19.5965
ORdate_year2009                                              1.231e+00  8.124e-01   0.15104   10.0319
ORdate_year2010                                              1.511e+00  6.619e-01   0.18502   12.3359
ORdate_year2011                                              1.905e+00  5.249e-01   0.22626   16.0414
ORdate_year2012                                              2.754e+00  3.631e-01   0.34618   21.9131
ORdate_year2013                                              2.427e+00  4.120e-01   0.30076   19.5918
ORdate_year2014                                              1.892e+00  5.286e-01   0.22264   16.0752
ORdate_year2015                                              9.000e-01  1.111e+00   0.08047   10.0652
ORdate_year2016                                              1.592e+00  6.283e-01   0.14188   17.8572
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.876e-01  1.127e+00   0.46464    1.6956
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.924e+00  5.198e-01   1.28853    2.8728
SmokerStatusEx-smoker                                        6.061e-01  1.650e+00   0.39508    0.9299
SmokerStatusNever smoked                                     4.636e-01  2.157e+00   0.24589    0.8741
Med.Statin.LLDno                                             1.485e+00  6.735e-01   0.95104    2.3180
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.041e+00  9.608e-01   0.60190    1.7999
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.889e-01  1.011e+00   0.97914    0.9988
BMI                                                          1.023e+00  9.779e-01   0.97235    1.0754
MedHx_CVDyes                                                 2.236e+00  4.472e-01   1.40828    3.5514
stenose0-49%                                                 5.406e-08  1.850e+07   0.00000       Inf
stenose50-70%                                                2.132e-01  4.691e+00   0.05998    0.7577
stenose70-90%                                                3.772e-01  2.651e+00   0.15830    0.8989
stenose90-99%                                                3.302e-01  3.029e+00   0.13730    0.7939
stenose100% (Occlusion)                                      8.204e-08  1.219e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                3.663e-08  2.730e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.75  (se = 0.021 )
Likelihood ratio test= 92.1  on 33 df,   p=2e-07
Wald test            = 77.43  on 33 df,   p=2e-05
Score (logrank) test = 99.33  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PTPRD ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: PTPRD 
Effect size...............: -0.194749 
Standard error............: 0.196415 
Odds ratio (effect size)..: 0.823 
Lower 95% CI..............: 0.56 
Upper 95% CI..............: 1.21 
T-value...................: -0.991513 
P-value...................: 0.3214349 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [CYP46A1]; 13 out of 35 target-of-interest.
   > cross tabulation of CYP46A1-stratum.

[4.75, 8.32) [8.32,11.38] 
         546          546 

   > fitting the model for CYP46A1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.32,11.38] -3.291e-01  7.196e-01  1.976e-01 -1.665 0.095864 .  
Age                                                            3.902e-02  1.040e+00  1.326e-02  2.943 0.003248 ** 
Gendermale                                                     4.700e-01  1.600e+00  2.381e-01  1.974 0.048418 *  
ORdate_year2002                                                1.130e+00  3.096e+00  1.103e+00  1.025 0.305435    
ORdate_year2003                                                1.114e-01  1.118e+00  1.133e+00  0.098 0.921683    
ORdate_year2004                                                4.108e-01  1.508e+00  1.087e+00  0.378 0.705503    
ORdate_year2005                                                9.266e-01  2.526e+00  1.058e+00  0.875 0.381366    
ORdate_year2006                                                9.914e-01  2.695e+00  1.044e+00  0.949 0.342467    
ORdate_year2007                                                1.854e-01  1.204e+00  1.087e+00  0.171 0.864610    
ORdate_year2008                                                1.010e+00  2.747e+00  1.057e+00  0.956 0.338910    
ORdate_year2009                                                2.661e-01  1.305e+00  1.070e+00  0.249 0.803611    
ORdate_year2010                                                5.235e-01  1.688e+00  1.072e+00  0.488 0.625325    
ORdate_year2011                                                7.193e-01  2.053e+00  1.088e+00  0.661 0.508661    
ORdate_year2012                                                1.123e+00  3.074e+00  1.059e+00  1.061 0.288911    
ORdate_year2013                                                9.054e-01  2.473e+00  1.066e+00  0.849 0.395683    
ORdate_year2014                                                5.927e-01  1.809e+00  1.090e+00  0.544 0.586645    
ORdate_year2015                                               -4.252e-02  9.584e-01  1.231e+00 -0.035 0.972453    
ORdate_year2016                                                4.978e-01  1.645e+00  1.234e+00  0.403 0.686581    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.248e-01  8.826e-01  3.299e-01 -0.378 0.705093    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.783e-01  1.971e+00  2.055e-01  3.301 0.000964 ***
SmokerStatusEx-smoker                                         -5.118e-01  5.994e-01  2.173e-01 -2.355 0.018512 *  
SmokerStatusNever smoked                                      -7.848e-01  4.562e-01  3.226e-01 -2.433 0.014975 *  
Med.Statin.LLDno                                               4.031e-01  1.496e+00  2.263e-01  1.781 0.074932 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         3.970e-02  1.041e+00  2.792e-01  0.142 0.886933    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.186e-02  9.882e-01  5.117e-03 -2.317 0.020485 *  
BMI                                                            2.236e-02  1.023e+00  2.570e-02  0.870 0.384235    
MedHx_CVDyes                                                   8.235e-01  2.278e+00  2.364e-01  3.484 0.000495 ***
stenose0-49%                                                  -1.681e+01  5.001e-08  2.574e+03 -0.007 0.994788    
stenose50-70%                                                 -1.571e+00  2.077e-01  6.509e-01 -2.414 0.015766 *  
stenose70-90%                                                 -9.501e-01  3.867e-01  4.499e-01 -2.112 0.034687 *  
stenose90-99%                                                 -1.087e+00  3.373e-01  4.527e-01 -2.400 0.016381 *  
stenose100% (Occlusion)                                       -1.631e+01  8.229e-08  2.299e+03 -0.007 0.994339    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.709e+01  3.786e-08  3.773e+03 -0.005 0.996386    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.32,11.38] 7.196e-01  1.390e+00   0.48851    1.0600
Age                                                           1.040e+00  9.617e-01   1.01312    1.0672
Gendermale                                                    1.600e+00  6.250e-01   1.00327    2.5517
ORdate_year2002                                               3.096e+00  3.230e-01   0.35659   26.8802
ORdate_year2003                                               1.118e+00  8.946e-01   0.12127   10.3045
ORdate_year2004                                               1.508e+00  6.631e-01   0.17909   12.6986
ORdate_year2005                                               2.526e+00  3.959e-01   0.31727   20.1090
ORdate_year2006                                               2.695e+00  3.711e-01   0.34803   20.8686
ORdate_year2007                                               1.204e+00  8.308e-01   0.14288   10.1404
ORdate_year2008                                               2.747e+00  3.641e-01   0.34632   21.7842
ORdate_year2009                                               1.305e+00  7.663e-01   0.16020   10.6287
ORdate_year2010                                               1.688e+00  5.924e-01   0.20643   13.8025
ORdate_year2011                                               2.053e+00  4.871e-01   0.24323   17.3279
ORdate_year2012                                               3.074e+00  3.253e-01   0.38577   24.4999
ORdate_year2013                                               2.473e+00  4.044e-01   0.30610   19.9770
ORdate_year2014                                               1.809e+00  5.529e-01   0.21358   15.3187
ORdate_year2015                                               9.584e-01  1.043e+00   0.08581   10.7039
ORdate_year2016                                               1.645e+00  6.079e-01   0.14659   18.4614
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.826e-01  1.133e+00   0.46240    1.6848
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.971e+00  5.075e-01   1.31728    2.9479
SmokerStatusEx-smoker                                         5.994e-01  1.668e+00   0.39151    0.9177
SmokerStatusNever smoked                                      4.562e-01  2.192e+00   0.24242    0.8585
Med.Statin.LLDno                                              1.496e+00  6.683e-01   0.96028    2.3319
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.041e+00  9.611e-01   0.60195    1.7986
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.882e-01  1.012e+00   0.97835    0.9982
BMI                                                           1.023e+00  9.779e-01   0.97238    1.0754
MedHx_CVDyes                                                  2.278e+00  4.389e-01   1.43356    3.6211
stenose0-49%                                                  5.001e-08  2.000e+07   0.00000       Inf
stenose50-70%                                                 2.077e-01  4.814e+00   0.05801    0.7440
stenose70-90%                                                 3.867e-01  2.586e+00   0.16011    0.9339
stenose90-99%                                                 3.373e-01  2.965e+00   0.13889    0.8192
stenose100% (Occlusion)                                       8.229e-08  1.215e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.786e-08  2.642e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.752  (se = 0.022 )
Likelihood ratio test= 93.9  on 33 df,   p=9e-08
Wald test            = 79.37  on 33 df,   p=1e-05
Score (logrank) test = 100.8  on 33 df,   p=9e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CYP46A1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: CYP46A1 
Effect size...............: -0.329079 
Standard error............: 0.197616 
Odds ratio (effect size)..: 0.72 
Lower 95% CI..............: 0.489 
Upper 95% CI..............: 1.06 
T-value...................: -1.665245 
P-value...................: 0.09586385 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [OR2T33]; 14 out of 35 target-of-interest.
   > cross tabulation of OR2T33-stratum.

[2.37,7.05) [7.05,9.61] 
        546         546 

   > fitting the model for OR2T33-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.05,9.61] -2.191e-01  8.032e-01  1.941e-01 -1.129 0.259019    
Age                                                           3.946e-02  1.040e+00  1.328e-02  2.971 0.002969 ** 
Gendermale                                                    4.594e-01  1.583e+00  2.377e-01  1.933 0.053229 .  
ORdate_year2002                                               1.034e+00  2.812e+00  1.100e+00  0.940 0.347063    
ORdate_year2003                                               3.988e-02  1.041e+00  1.132e+00  0.035 0.971897    
ORdate_year2004                                               3.493e-01  1.418e+00  1.087e+00  0.321 0.747940    
ORdate_year2005                                               8.681e-01  2.382e+00  1.059e+00  0.820 0.412265    
ORdate_year2006                                               9.545e-01  2.597e+00  1.044e+00  0.914 0.360697    
ORdate_year2007                                               1.095e-01  1.116e+00  1.086e+00  0.101 0.919735    
ORdate_year2008                                               9.380e-01  2.555e+00  1.055e+00  0.889 0.374046    
ORdate_year2009                                               2.164e-01  1.242e+00  1.070e+00  0.202 0.839746    
ORdate_year2010                                               4.487e-01  1.566e+00  1.071e+00  0.419 0.675311    
ORdate_year2011                                               6.623e-01  1.939e+00  1.087e+00  0.609 0.542482    
ORdate_year2012                                               1.055e+00  2.871e+00  1.058e+00  0.997 0.318813    
ORdate_year2013                                               8.736e-01  2.396e+00  1.066e+00  0.819 0.412597    
ORdate_year2014                                               5.987e-01  1.820e+00  1.090e+00  0.549 0.582892    
ORdate_year2015                                              -3.114e-02  9.693e-01  1.232e+00 -0.025 0.979836    
ORdate_year2016                                               4.591e-01  1.583e+00  1.233e+00  0.372 0.709705    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.226e-01  8.846e-01  3.302e-01 -0.371 0.710421    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.889e-01  1.992e+00  2.076e-01  3.318 0.000905 ***
SmokerStatusEx-smoker                                        -5.140e-01  5.981e-01  2.176e-01 -2.362 0.018178 *  
SmokerStatusNever smoked                                     -7.702e-01  4.629e-01  3.228e-01 -2.386 0.017048 *  
Med.Statin.LLDno                                              4.152e-01  1.515e+00  2.264e-01  1.834 0.066673 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        4.687e-02  1.048e+00  2.795e-01  0.168 0.866833    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.108e-02  9.890e-01  5.082e-03 -2.180 0.029262 *  
BMI                                                           2.198e-02  1.022e+00  2.587e-02  0.850 0.395549    
MedHx_CVDyes                                                  8.281e-01  2.289e+00  2.363e-01  3.504 0.000459 ***
stenose0-49%                                                 -1.678e+01  5.151e-08  2.560e+03 -0.007 0.994769    
stenose50-70%                                                -1.555e+00  2.112e-01  6.499e-01 -2.393 0.016716 *  
stenose70-90%                                                -9.836e-01  3.740e-01  4.463e-01 -2.204 0.027521 *  
stenose90-99%                                                -1.120e+00  3.262e-01  4.503e-01 -2.488 0.012852 *  
stenose100% (Occlusion)                                      -1.633e+01  8.118e-08  2.266e+03 -0.007 0.994250    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.713e+01  3.628e-08  3.736e+03 -0.005 0.996342    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.05,9.61] 8.032e-01  1.245e+00   0.54901    1.1751
Age                                                          1.040e+00  9.613e-01   1.01352    1.0677
Gendermale                                                   1.583e+00  6.316e-01   0.99363    2.5226
ORdate_year2002                                              2.812e+00  3.556e-01   0.32585   24.2732
ORdate_year2003                                              1.041e+00  9.609e-01   0.11320    9.5674
ORdate_year2004                                              1.418e+00  7.052e-01   0.16848   11.9348
ORdate_year2005                                              2.382e+00  4.198e-01   0.29909   18.9753
ORdate_year2006                                              2.597e+00  3.850e-01   0.33546   20.1109
ORdate_year2007                                              1.116e+00  8.963e-01   0.13271    9.3791
ORdate_year2008                                              2.555e+00  3.914e-01   0.32297   20.2114
ORdate_year2009                                              1.242e+00  8.054e-01   0.15249   10.1084
ORdate_year2010                                              1.566e+00  6.385e-01   0.19192   12.7813
ORdate_year2011                                              1.939e+00  5.157e-01   0.23018   16.3364
ORdate_year2012                                              2.871e+00  3.483e-01   0.36095   22.8423
ORdate_year2013                                              2.396e+00  4.174e-01   0.29635   19.3651
ORdate_year2014                                              1.820e+00  5.495e-01   0.21478   15.4187
ORdate_year2015                                              9.693e-01  1.032e+00   0.08662   10.8470
ORdate_year2016                                              1.583e+00  6.318e-01   0.14110   17.7529
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.846e-01  1.130e+00   0.46314    1.6897
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.992e+00  5.021e-01   1.32581    2.9917
SmokerStatusEx-smoker                                        5.981e-01  1.672e+00   0.39044    0.9162
SmokerStatusNever smoked                                     4.629e-01  2.160e+00   0.24588    0.8716
Med.Statin.LLDno                                             1.515e+00  6.602e-01   0.97185    2.3609
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.048e+00  9.542e-01   0.60596    1.8124
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.890e-01  1.011e+00   0.97918    0.9989
BMI                                                          1.022e+00  9.783e-01   0.97168    1.0754
MedHx_CVDyes                                                 2.289e+00  4.369e-01   1.44032    3.6375
stenose0-49%                                                 5.151e-08  1.941e+07   0.00000       Inf
stenose50-70%                                                2.112e-01  4.735e+00   0.05909    0.7548
stenose70-90%                                                3.740e-01  2.674e+00   0.15595    0.8968
stenose90-99%                                                3.262e-01  3.066e+00   0.13492    0.7884
stenose100% (Occlusion)                                      8.118e-08  1.232e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                3.628e-08  2.757e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.749  (se = 0.022 )
Likelihood ratio test= 92.39  on 33 df,   p=2e-07
Wald test            = 77.02  on 33 df,   p=2e-05
Score (logrank) test = 98.98  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' OR2T33 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: OR2T33 
Effect size...............: -0.219132 
Standard error............: 0.194143 
Odds ratio (effect size)..: 0.803 
Lower 95% CI..............: 0.549 
Upper 95% CI..............: 1.175 
T-value...................: -1.128714 
P-value...................: 0.2590186 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [SORBS2]; 15 out of 35 target-of-interest.
   > cross tabulation of SORBS2-stratum.

[5.68, 7.38) [7.38,11.45] 
         546          546 

   > fitting the model for SORBS2-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.38,11.45] -2.024e-01  8.168e-01  1.995e-01 -1.014 0.310443    
Age                                                            4.043e-02  1.041e+00  1.329e-02  3.043 0.002345 ** 
Gendermale                                                     4.254e-01  1.530e+00  2.390e-01  1.780 0.075017 .  
ORdate_year2002                                                9.364e-01  2.551e+00  1.102e+00  0.850 0.395321    
ORdate_year2003                                               -3.993e-02  9.609e-01  1.134e+00 -0.035 0.971926    
ORdate_year2004                                                3.152e-01  1.370e+00  1.090e+00  0.289 0.772487    
ORdate_year2005                                                8.390e-01  2.314e+00  1.062e+00  0.790 0.429445    
ORdate_year2006                                                8.800e-01  2.411e+00  1.049e+00  0.839 0.401660    
ORdate_year2007                                                4.763e-02  1.049e+00  1.089e+00  0.044 0.965107    
ORdate_year2008                                                8.520e-01  2.344e+00  1.059e+00  0.804 0.421296    
ORdate_year2009                                                1.711e-01  1.187e+00  1.074e+00  0.159 0.873438    
ORdate_year2010                                                3.855e-01  1.470e+00  1.075e+00  0.359 0.719849    
ORdate_year2011                                                6.039e-01  1.829e+00  1.090e+00  0.554 0.579423    
ORdate_year2012                                                1.047e+00  2.850e+00  1.059e+00  0.989 0.322728    
ORdate_year2013                                                9.146e-01  2.496e+00  1.068e+00  0.857 0.391611    
ORdate_year2014                                                5.987e-01  1.820e+00  1.093e+00  0.548 0.584027    
ORdate_year2015                                               -7.905e-02  9.240e-01  1.233e+00 -0.064 0.948887    
ORdate_year2016                                                4.280e-01  1.534e+00  1.235e+00  0.347 0.728834    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.327e-01  8.757e-01  3.304e-01 -0.402 0.687837    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.502e-01  1.916e+00  2.041e-01  3.186 0.001443 ** 
SmokerStatusEx-smoker                                         -5.107e-01  6.001e-01  2.181e-01 -2.342 0.019203 *  
SmokerStatusNever smoked                                      -7.480e-01  4.733e-01  3.232e-01 -2.315 0.020632 *  
Med.Statin.LLDno                                               4.138e-01  1.513e+00  2.265e-01  1.827 0.067668 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         4.270e-02  1.044e+00  2.797e-01  0.153 0.878689    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.067e-02  9.894e-01  5.040e-03 -2.118 0.034181 *  
BMI                                                            2.309e-02  1.023e+00  2.579e-02  0.895 0.370592    
MedHx_CVDyes                                                   8.133e-01  2.255e+00  2.357e-01  3.450 0.000561 ***
stenose0-49%                                                  -1.669e+01  5.619e-08  2.567e+03 -0.007 0.994811    
stenose50-70%                                                 -1.614e+00  1.990e-01  6.486e-01 -2.489 0.012807 *  
stenose70-90%                                                 -1.002e+00  3.673e-01  4.425e-01 -2.264 0.023586 *  
stenose90-99%                                                 -1.140e+00  3.199e-01  4.487e-01 -2.540 0.011082 *  
stenose100% (Occlusion)                                       -1.633e+01  8.076e-08  2.248e+03 -0.007 0.994203    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.712e+01  3.665e-08  3.730e+03 -0.005 0.996337    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.38,11.45] 8.168e-01  1.224e+00   0.55239    1.2077
Age                                                           1.041e+00  9.604e-01   1.01449    1.0687
Gendermale                                                    1.530e+00  6.535e-01   0.95799    2.4443
ORdate_year2002                                               2.551e+00  3.920e-01   0.29440   22.1019
ORdate_year2003                                               9.609e-01  1.041e+00   0.10400    8.8778
ORdate_year2004                                               1.370e+00  7.297e-01   0.16181   11.6073
ORdate_year2005                                               2.314e+00  4.322e-01   0.28878   18.5414
ORdate_year2006                                               2.411e+00  4.148e-01   0.30832   18.8532
ORdate_year2007                                               1.049e+00  9.535e-01   0.12415    8.8601
ORdate_year2008                                               2.344e+00  4.266e-01   0.29391   18.6987
ORdate_year2009                                               1.187e+00  8.427e-01   0.14455    9.7407
ORdate_year2010                                               1.470e+00  6.801e-01   0.17884   12.0888
ORdate_year2011                                               1.829e+00  5.467e-01   0.21614   15.4821
ORdate_year2012                                               2.850e+00  3.508e-01   0.35749   22.7284
ORdate_year2013                                               2.496e+00  4.007e-01   0.30794   20.2289
ORdate_year2014                                               1.820e+00  5.495e-01   0.21344   15.5138
ORdate_year2015                                               9.240e-01  1.082e+00   0.08241   10.3598
ORdate_year2016                                               1.534e+00  6.518e-01   0.13645   17.2504
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.757e-01  1.142e+00   0.45828    1.6733
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.916e+00  5.219e-01   1.28430    2.8583
SmokerStatusEx-smoker                                         6.001e-01  1.666e+00   0.39133    0.9201
SmokerStatusNever smoked                                      4.733e-01  2.113e+00   0.25123    0.8917
Med.Statin.LLDno                                              1.513e+00  6.611e-01   0.97038    2.3578
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.044e+00  9.582e-01   0.60316    1.8057
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.894e-01  1.011e+00   0.97966    0.9992
BMI                                                           1.023e+00  9.772e-01   0.97292    1.0764
MedHx_CVDyes                                                  2.255e+00  4.434e-01   1.42076    3.5798
stenose0-49%                                                  5.619e-08  1.780e+07   0.00000       Inf
stenose50-70%                                                 1.990e-01  5.025e+00   0.05581    0.7095
stenose70-90%                                                 3.673e-01  2.723e+00   0.15428    0.8742
stenose90-99%                                                 3.199e-01  3.126e+00   0.13278    0.7708
stenose100% (Occlusion)                                       8.076e-08  1.238e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.665e-08  2.729e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.752  (se = 0.021 )
Likelihood ratio test= 92.15  on 33 df,   p=2e-07
Wald test            = 76.88  on 33 df,   p=2e-05
Score (logrank) test = 98.74  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' SORBS2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: SORBS2 
Effect size...............: -0.202396 
Standard error............: 0.199544 
Odds ratio (effect size)..: 0.817 
Lower 95% CI..............: 0.552 
Upper 95% CI..............: 1.208 
T-value...................: -1.014292 
P-value...................: 0.3104433 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [ITGA7]; 16 out of 35 target-of-interest.
   > cross tabulation of ITGA7-stratum.

[4.65, 8.32) [8.32,11.04] 
         546          546 

   > fitting the model for ITGA7-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.32,11.04] -2.992e-01  7.414e-01  1.953e-01 -1.532 0.125458    
Age                                                            3.976e-02  1.041e+00  1.327e-02  2.995 0.002740 ** 
Gendermale                                                     4.461e-01  1.562e+00  2.380e-01  1.875 0.060859 .  
ORdate_year2002                                                1.023e+00  2.783e+00  1.099e+00  0.931 0.351855    
ORdate_year2003                                                4.426e-02  1.045e+00  1.132e+00  0.039 0.968804    
ORdate_year2004                                                3.377e-01  1.402e+00  1.087e+00  0.311 0.756077    
ORdate_year2005                                                8.457e-01  2.330e+00  1.059e+00  0.799 0.424436    
ORdate_year2006                                                9.199e-01  2.509e+00  1.044e+00  0.881 0.378441    
ORdate_year2007                                                6.688e-02  1.069e+00  1.087e+00  0.062 0.950918    
ORdate_year2008                                                9.195e-01  2.508e+00  1.055e+00  0.871 0.383606    
ORdate_year2009                                                1.783e-01  1.195e+00  1.070e+00  0.167 0.867728    
ORdate_year2010                                                4.639e-01  1.590e+00  1.071e+00  0.433 0.664917    
ORdate_year2011                                                6.443e-01  1.905e+00  1.087e+00  0.593 0.553287    
ORdate_year2012                                                1.066e+00  2.903e+00  1.059e+00  1.007 0.314082    
ORdate_year2013                                                8.985e-01  2.456e+00  1.065e+00  0.844 0.398846    
ORdate_year2014                                                6.604e-01  1.935e+00  1.090e+00  0.606 0.544773    
ORdate_year2015                                               -1.322e-02  9.869e-01  1.232e+00 -0.011 0.991440    
ORdate_year2016                                                5.227e-01  1.687e+00  1.235e+00  0.423 0.672057    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.017e-01  9.033e-01  3.301e-01 -0.308 0.757955    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.731e-01  1.960e+00  2.050e-01  3.283 0.001026 ** 
SmokerStatusEx-smoker                                         -4.925e-01  6.111e-01  2.173e-01 -2.267 0.023419 *  
SmokerStatusNever smoked                                      -7.693e-01  4.633e-01  3.228e-01 -2.383 0.017179 *  
Med.Statin.LLDno                                               4.117e-01  1.509e+00  2.267e-01  1.816 0.069328 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         5.359e-02  1.055e+00  2.798e-01  0.192 0.848106    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.142e-02  9.886e-01  5.102e-03 -2.239 0.025171 *  
BMI                                                            2.265e-02  1.023e+00  2.571e-02  0.881 0.378388    
MedHx_CVDyes                                                   8.290e-01  2.291e+00  2.364e-01  3.507 0.000453 ***
stenose0-49%                                                  -1.774e+01  1.965e-08  4.090e+03 -0.004 0.996538    
stenose50-70%                                                 -1.568e+00  2.085e-01  6.490e-01 -2.416 0.015687 *  
stenose70-90%                                                 -9.590e-01  3.833e-01  4.452e-01 -2.154 0.031235 *  
stenose90-99%                                                 -1.111e+00  3.292e-01  4.492e-01 -2.473 0.013389 *  
stenose100% (Occlusion)                                       -1.725e+01  3.212e-08  3.743e+03 -0.005 0.996322    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.810e+01  1.378e-08  6.185e+03 -0.003 0.997665    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][8.32,11.04] 7.414e-01  1.349e+00   0.50565    1.0871
Age                                                           1.041e+00  9.610e-01   1.01384    1.0680
Gendermale                                                    1.562e+00  6.401e-01   0.97987    2.4907
ORdate_year2002                                               2.783e+00  3.594e-01   0.32268   23.9955
ORdate_year2003                                               1.045e+00  9.567e-01   0.11371    9.6084
ORdate_year2004                                               1.402e+00  7.134e-01   0.16650   11.7996
ORdate_year2005                                               2.330e+00  4.292e-01   0.29244   18.5589
ORdate_year2006                                               2.509e+00  3.985e-01   0.32395   19.4339
ORdate_year2007                                               1.069e+00  9.353e-01   0.12711    8.9931
ORdate_year2008                                               2.508e+00  3.987e-01   0.31696   19.8472
ORdate_year2009                                               1.195e+00  8.367e-01   0.14663    9.7416
ORdate_year2010                                               1.590e+00  6.288e-01   0.19490   12.9756
ORdate_year2011                                               1.905e+00  5.250e-01   0.22632   16.0297
ORdate_year2012                                               2.903e+00  3.445e-01   0.36452   23.1191
ORdate_year2013                                               2.456e+00  4.072e-01   0.30457   19.8053
ORdate_year2014                                               1.935e+00  5.167e-01   0.22837   16.4037
ORdate_year2015                                               9.869e-01  1.013e+00   0.08817   11.0452
ORdate_year2016                                               1.687e+00  5.929e-01   0.14998   18.9648
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      9.033e-01  1.107e+00   0.47295    1.7251
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.960e+00  5.101e-01   1.31168    2.9299
SmokerStatusEx-smoker                                         6.111e-01  1.636e+00   0.39917    0.9356
SmokerStatusNever smoked                                      4.633e-01  2.158e+00   0.24610    0.8724
Med.Statin.LLDno                                              1.509e+00  6.625e-01   0.96796    2.3536
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.055e+00  9.478e-01   0.60971    1.8257
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.886e-01  1.011e+00   0.97880    0.9986
BMI                                                           1.023e+00  9.776e-01   0.97263    1.0758
MedHx_CVDyes                                                  2.291e+00  4.365e-01   1.44153    3.6412
stenose0-49%                                                  1.965e-08  5.088e+07   0.00000       Inf
stenose50-70%                                                 2.085e-01  4.797e+00   0.05842    0.7438
stenose70-90%                                                 3.833e-01  2.609e+00   0.16015    0.9172
stenose90-99%                                                 3.292e-01  3.038e+00   0.13648    0.7941
stenose100% (Occlusion)                                       3.212e-08  3.113e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.378e-08  7.256e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.752  (se = 0.021 )
Likelihood ratio test= 93.48  on 33 df,   p=1e-07
Wald test            = 78.04  on 33 df,   p=2e-05
Score (logrank) test = 99.88  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' ITGA7 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: ITGA7 
Effect size...............: -0.299195 
Standard error............: 0.195264 
Odds ratio (effect size)..: 0.741 
Lower 95% CI..............: 0.506 
Upper 95% CI..............: 1.087 
T-value...................: -1.53226 
P-value...................: 0.1254582 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [FOS]; 17 out of 35 target-of-interest.
   > cross tabulation of FOS-stratum.

[2.37, 6.06) [6.06,11.07] 
         546          546 

   > fitting the model for FOS-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.06,11.07]  7.304e-02  1.076e+00  2.070e-01  0.353 0.724216    
Age                                                            4.032e-02  1.041e+00  1.335e-02  3.020 0.002531 ** 
Gendermale                                                     4.576e-01  1.580e+00  2.371e-01  1.930 0.053570 .  
ORdate_year2002                                                1.019e+00  2.772e+00  1.106e+00  0.922 0.356764    
ORdate_year2003                                                3.941e-02  1.040e+00  1.136e+00  0.035 0.972315    
ORdate_year2004                                                3.866e-01  1.472e+00  1.093e+00  0.354 0.723492    
ORdate_year2005                                                9.180e-01  2.504e+00  1.064e+00  0.863 0.388313    
ORdate_year2006                                                1.007e+00  2.736e+00  1.056e+00  0.954 0.340309    
ORdate_year2007                                                1.545e-01  1.167e+00  1.101e+00  0.140 0.888344    
ORdate_year2008                                                9.733e-01  2.647e+00  1.066e+00  0.913 0.361066    
ORdate_year2009                                                2.794e-01  1.322e+00  1.079e+00  0.259 0.795770    
ORdate_year2010                                                4.933e-01  1.638e+00  1.081e+00  0.457 0.648028    
ORdate_year2011                                                6.746e-01  1.963e+00  1.094e+00  0.617 0.537564    
ORdate_year2012                                                1.081e+00  2.948e+00  1.062e+00  1.018 0.308550    
ORdate_year2013                                                9.290e-01  2.532e+00  1.068e+00  0.870 0.384404    
ORdate_year2014                                                6.163e-01  1.852e+00  1.092e+00  0.565 0.572402    
ORdate_year2015                                               -3.707e-02  9.636e-01  1.236e+00 -0.030 0.976065    
ORdate_year2016                                                4.360e-01  1.546e+00  1.234e+00  0.353 0.723774    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.204e-01  8.866e-01  3.299e-01 -0.365 0.715238    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.554e-01  1.926e+00  2.053e-01  3.192 0.001412 ** 
SmokerStatusEx-smoker                                         -5.104e-01  6.002e-01  2.179e-01 -2.343 0.019144 *  
SmokerStatusNever smoked                                      -7.615e-01  4.670e-01  3.227e-01 -2.360 0.018273 *  
Med.Statin.LLDno                                               4.120e-01  1.510e+00  2.270e-01  1.815 0.069526 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         4.711e-02  1.048e+00  2.798e-01  0.168 0.866284    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.087e-02  9.892e-01  5.053e-03 -2.150 0.031521 *  
BMI                                                            2.194e-02  1.022e+00  2.580e-02  0.850 0.395107    
MedHx_CVDyes                                                   8.190e-01  2.268e+00  2.359e-01  3.471 0.000518 ***
stenose0-49%                                                  -1.670e+01  5.587e-08  2.565e+03 -0.007 0.994805    
stenose50-70%                                                 -1.564e+00  2.093e-01  6.488e-01 -2.411 0.015919 *  
stenose70-90%                                                 -9.822e-01  3.745e-01  4.443e-01 -2.211 0.027062 *  
stenose90-99%                                                 -1.115e+00  3.281e-01  4.489e-01 -2.483 0.013038 *  
stenose100% (Occlusion)                                       -1.633e+01  8.103e-08  2.236e+03 -0.007 0.994173    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.720e+01  3.389e-08  3.729e+03 -0.005 0.996319    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.06,11.07] 1.076e+00  9.296e-01   0.71698    1.6141
Age                                                           1.041e+00  9.605e-01   1.01425    1.0687
Gendermale                                                    1.580e+00  6.328e-01   0.99299    2.5149
ORdate_year2002                                               2.772e+00  3.608e-01   0.31706   24.2263
ORdate_year2003                                               1.040e+00  9.614e-01   0.11231    9.6341
ORdate_year2004                                               1.472e+00  6.794e-01   0.17292   12.5289
ORdate_year2005                                               2.504e+00  3.993e-01   0.31110   20.1586
ORdate_year2006                                               2.736e+00  3.655e-01   0.34566   21.6573
ORdate_year2007                                               1.167e+00  8.568e-01   0.13501   10.0891
ORdate_year2008                                               2.647e+00  3.778e-01   0.32781   21.3671
ORdate_year2009                                               1.322e+00  7.563e-01   0.15942   10.9678
ORdate_year2010                                               1.638e+00  6.106e-01   0.19701   13.6132
ORdate_year2011                                               1.963e+00  5.094e-01   0.22990   16.7657
ORdate_year2012                                               2.948e+00  3.392e-01   0.36796   23.6162
ORdate_year2013                                               2.532e+00  3.949e-01   0.31212   20.5400
ORdate_year2014                                               1.852e+00  5.399e-01   0.21794   15.7403
ORdate_year2015                                               9.636e-01  1.038e+00   0.08555   10.8535
ORdate_year2016                                               1.546e+00  6.466e-01   0.13782   17.3523
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.866e-01  1.128e+00   0.46441    1.6926
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.926e+00  5.192e-01   1.28788    2.8802
SmokerStatusEx-smoker                                         6.002e-01  1.666e+00   0.39162    0.9200
SmokerStatusNever smoked                                      4.670e-01  2.141e+00   0.24811    0.8789
Med.Statin.LLDno                                              1.510e+00  6.623e-01   0.96762    2.3559
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.048e+00  9.540e-01   0.60574    1.8140
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.892e-01  1.011e+00   0.97944    0.9990
BMI                                                           1.022e+00  9.783e-01   0.97177    1.0752
MedHx_CVDyes                                                  2.268e+00  4.409e-01   1.42837    3.6016
stenose0-49%                                                  5.587e-08  1.790e+07   0.00000       Inf
stenose50-70%                                                 2.093e-01  4.778e+00   0.05868    0.7464
stenose70-90%                                                 3.745e-01  2.670e+00   0.15677    0.8946
stenose90-99%                                                 3.281e-01  3.048e+00   0.13610    0.7908
stenose100% (Occlusion)                                       8.103e-08  1.234e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.389e-08  2.950e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.748  (se = 0.021 )
Likelihood ratio test= 91.24  on 33 df,   p=2e-07
Wald test            = 76.2  on 33 df,   p=3e-05
Score (logrank) test = 97.93  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' FOS ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: FOS 
Effect size...............: 0.073043 
Standard error............: 0.207022 
Odds ratio (effect size)..: 1.076 
Lower 95% CI..............: 0.717 
Upper 95% CI..............: 1.614 
T-value...................: 0.35283 
P-value...................: 0.7242162 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [HMOX1]; 18 out of 35 target-of-interest.
   > cross tabulation of HMOX1-stratum.

[4.36, 7.02) [7.02,11.50] 
         546          546 

   > fitting the model for HMOX1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.02,11.50]  1.592e-01  1.173e+00  1.950e-01  0.816 0.414236    
Age                                                            4.045e-02  1.041e+00  1.330e-02  3.040 0.002363 ** 
Gendermale                                                     4.581e-01  1.581e+00  2.370e-01  1.933 0.053208 .  
ORdate_year2002                                                9.946e-01  2.704e+00  1.099e+00  0.905 0.365363    
ORdate_year2003                                                3.604e-03  1.004e+00  1.132e+00  0.003 0.997459    
ORdate_year2004                                                3.421e-01  1.408e+00  1.087e+00  0.315 0.752998    
ORdate_year2005                                                8.691e-01  2.385e+00  1.059e+00  0.821 0.411826    
ORdate_year2006                                                9.343e-01  2.545e+00  1.045e+00  0.894 0.371148    
ORdate_year2007                                                1.001e-01  1.105e+00  1.086e+00  0.092 0.926538    
ORdate_year2008                                                9.075e-01  2.478e+00  1.055e+00  0.860 0.389751    
ORdate_year2009                                                2.156e-01  1.241e+00  1.071e+00  0.201 0.840352    
ORdate_year2010                                                4.375e-01  1.549e+00  1.071e+00  0.409 0.682893    
ORdate_year2011                                                6.497e-01  1.915e+00  1.088e+00  0.597 0.550218    
ORdate_year2012                                                1.041e+00  2.832e+00  1.058e+00  0.984 0.324891    
ORdate_year2013                                                8.453e-01  2.329e+00  1.069e+00  0.791 0.428898    
ORdate_year2014                                                5.881e-01  1.801e+00  1.091e+00  0.539 0.589869    
ORdate_year2015                                               -6.077e-02  9.410e-01  1.232e+00 -0.049 0.960657    
ORdate_year2016                                                4.244e-01  1.529e+00  1.233e+00  0.344 0.730662    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.128e-01  8.933e-01  3.303e-01 -0.342 0.732702    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.636e-01  1.942e+00  2.052e-01  3.234 0.001222 ** 
SmokerStatusEx-smoker                                         -5.288e-01  5.893e-01  2.188e-01 -2.417 0.015661 *  
SmokerStatusNever smoked                                      -7.606e-01  4.674e-01  3.230e-01 -2.355 0.018509 *  
Med.Statin.LLDno                                               4.069e-01  1.502e+00  2.262e-01  1.799 0.072056 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         3.544e-02  1.036e+00  2.802e-01  0.126 0.899370    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.098e-02  9.891e-01  5.085e-03 -2.160 0.030766 *  
BMI                                                            2.270e-02  1.023e+00  2.585e-02  0.878 0.379893    
MedHx_CVDyes                                                   8.182e-01  2.266e+00  2.360e-01  3.467 0.000526 ***
stenose0-49%                                                  -1.773e+01  1.997e-08  4.198e+03 -0.004 0.996630    
stenose50-70%                                                 -1.601e+00  2.018e-01  6.497e-01 -2.464 0.013755 *  
stenose70-90%                                                 -1.027e+00  3.580e-01  4.474e-01 -2.296 0.021679 *  
stenose90-99%                                                 -1.155e+00  3.152e-01  4.511e-01 -2.560 0.010471 *  
stenose100% (Occlusion)                                       -1.732e+01  3.012e-08  3.666e+03 -0.005 0.996230    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.824e+01  1.198e-08  6.163e+03 -0.003 0.997639    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.02,11.50] 1.173e+00  8.528e-01   0.80017    1.7182
Age                                                           1.041e+00  9.604e-01   1.01448    1.0688
Gendermale                                                    1.581e+00  6.325e-01   0.99368    2.5156
ORdate_year2002                                               2.704e+00  3.699e-01   0.31380   23.2953
ORdate_year2003                                               1.004e+00  9.964e-01   0.10918    9.2254
ORdate_year2004                                               1.408e+00  7.102e-01   0.16716   11.8593
ORdate_year2005                                               2.385e+00  4.193e-01   0.29926   19.0022
ORdate_year2006                                               2.545e+00  3.929e-01   0.32849   19.7233
ORdate_year2007                                               1.105e+00  9.047e-01   0.13151    9.2903
ORdate_year2008                                               2.478e+00  4.036e-01   0.31333   19.5974
ORdate_year2009                                               1.241e+00  8.060e-01   0.15221   10.1128
ORdate_year2010                                               1.549e+00  6.456e-01   0.18984   12.6371
ORdate_year2011                                               1.915e+00  5.222e-01   0.22722   16.1404
ORdate_year2012                                               2.832e+00  3.531e-01   0.35642   22.5074
ORdate_year2013                                               2.329e+00  4.294e-01   0.28678   18.9103
ORdate_year2014                                               1.801e+00  5.554e-01   0.21220   15.2776
ORdate_year2015                                               9.410e-01  1.063e+00   0.08414   10.5252
ORdate_year2016                                               1.529e+00  6.541e-01   0.13640   17.1332
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.933e-01  1.119e+00   0.46761    1.7066
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.942e+00  5.150e-01   1.29874    2.9035
SmokerStatusEx-smoker                                         5.893e-01  1.697e+00   0.38379    0.9049
SmokerStatusNever smoked                                      4.674e-01  2.140e+00   0.24818    0.8801
Med.Statin.LLDno                                              1.502e+00  6.657e-01   0.96419    2.3404
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.036e+00  9.652e-01   0.59822    1.7944
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.891e-01  1.011e+00   0.97927    0.9990
BMI                                                           1.023e+00  9.776e-01   0.97242    1.0761
MedHx_CVDyes                                                  2.266e+00  4.412e-01   1.42714    3.5995
stenose0-49%                                                  1.997e-08  5.008e+07   0.00000       Inf
stenose50-70%                                                 2.018e-01  4.956e+00   0.05647    0.7209
stenose70-90%                                                 3.580e-01  2.793e+00   0.14895    0.8604
stenose90-99%                                                 3.152e-01  3.173e+00   0.13020    0.7629
stenose100% (Occlusion)                                       3.012e-08  3.320e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.198e-08  8.345e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.746  (se = 0.021 )
Likelihood ratio test= 91.78  on 33 df,   p=2e-07
Wald test            = 76.29  on 33 df,   p=3e-05
Score (logrank) test = 98.38  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HMOX1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: HMOX1 
Effect size...............: 0.159176 
Standard error............: 0.194958 
Odds ratio (effect size)..: 1.173 
Lower 95% CI..............: 0.8 
Upper 95% CI..............: 1.718 
T-value...................: 0.816462 
P-value...................: 0.4142357 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [LAPTM5]; 19 out of 35 target-of-interest.
   > cross tabulation of LAPTM5-stratum.

[2.37, 6.71) [6.71,11.74] 
         546          546 

   > fitting the model for LAPTM5-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.71,11.74]  3.571e-01  1.429e+00  1.977e-01  1.807 0.070812 .  
Age                                                            4.240e-02  1.043e+00  1.340e-02  3.163 0.001560 ** 
Gendermale                                                     4.460e-01  1.562e+00  2.374e-01  1.879 0.060296 .  
ORdate_year2002                                                1.015e+00  2.759e+00  1.097e+00  0.925 0.354872    
ORdate_year2003                                                3.076e-03  1.003e+00  1.130e+00  0.003 0.997829    
ORdate_year2004                                                3.460e-01  1.413e+00  1.086e+00  0.319 0.749897    
ORdate_year2005                                                8.803e-01  2.412e+00  1.057e+00  0.833 0.404893    
ORdate_year2006                                                9.617e-01  2.616e+00  1.043e+00  0.922 0.356485    
ORdate_year2007                                                1.955e-01  1.216e+00  1.087e+00  0.180 0.857260    
ORdate_year2008                                                9.540e-01  2.596e+00  1.053e+00  0.906 0.365076    
ORdate_year2009                                                2.666e-01  1.306e+00  1.069e+00  0.249 0.803033    
ORdate_year2010                                                4.423e-01  1.556e+00  1.069e+00  0.414 0.679190    
ORdate_year2011                                                6.456e-01  1.907e+00  1.086e+00  0.595 0.552172    
ORdate_year2012                                                1.100e+00  3.004e+00  1.056e+00  1.041 0.297721    
ORdate_year2013                                                8.105e-01  2.249e+00  1.066e+00  0.760 0.447001    
ORdate_year2014                                                5.411e-01  1.718e+00  1.090e+00  0.497 0.619437    
ORdate_year2015                                               -1.383e-02  9.863e-01  1.232e+00 -0.011 0.991038    
ORdate_year2016                                                4.215e-01  1.524e+00  1.232e+00  0.342 0.732284    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.214e-01  8.857e-01  3.300e-01 -0.368 0.712889    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.667e-01  1.948e+00  2.050e-01  3.253 0.001142 ** 
SmokerStatusEx-smoker                                         -5.341e-01  5.862e-01  2.178e-01 -2.453 0.014179 *  
SmokerStatusNever smoked                                      -7.849e-01  4.562e-01  3.224e-01 -2.435 0.014910 *  
Med.Statin.LLDno                                               4.281e-01  1.534e+00  2.267e-01  1.888 0.058995 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         1.989e-02  1.020e+00  2.810e-01  0.071 0.943561    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.117e-02  9.889e-01  5.132e-03 -2.176 0.029556 *  
BMI                                                            2.365e-02  1.024e+00  2.574e-02  0.919 0.358159    
MedHx_CVDyes                                                   8.335e-01  2.301e+00  2.365e-01  3.525 0.000424 ***
stenose0-49%                                                  -1.774e+01  1.968e-08  4.152e+03 -0.004 0.996590    
stenose50-70%                                                 -1.648e+00  1.925e-01  6.499e-01 -2.535 0.011230 *  
stenose70-90%                                                 -1.048e+00  3.507e-01  4.474e-01 -2.342 0.019171 *  
stenose90-99%                                                 -1.165e+00  3.121e-01  4.512e-01 -2.581 0.009853 ** 
stenose100% (Occlusion)                                       -1.729e+01  3.090e-08  3.658e+03 -0.005 0.996229    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.820e+01  1.241e-08  6.119e+03 -0.003 0.997626    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.71,11.74] 1.429e+00  6.997e-01   0.97016    2.1054
Age                                                           1.043e+00  9.585e-01   1.01626    1.0711
Gendermale                                                    1.562e+00  6.402e-01   0.98087    2.4874
ORdate_year2002                                               2.759e+00  3.625e-01   0.32144   23.6738
ORdate_year2003                                               1.003e+00  9.969e-01   0.10943    9.1949
ORdate_year2004                                               1.413e+00  7.075e-01   0.16837   11.8661
ORdate_year2005                                               2.412e+00  4.146e-01   0.30385   19.1429
ORdate_year2006                                               2.616e+00  3.823e-01   0.33877   20.2015
ORdate_year2007                                               1.216e+00  8.224e-01   0.14444   10.2358
ORdate_year2008                                               2.596e+00  3.852e-01   0.32944   20.4564
ORdate_year2009                                               1.306e+00  7.660e-01   0.16067   10.6079
ORdate_year2010                                               1.556e+00  6.426e-01   0.19133   12.6579
ORdate_year2011                                               1.907e+00  5.244e-01   0.22701   16.0221
ORdate_year2012                                               3.004e+00  3.328e-01   0.37890   23.8241
ORdate_year2013                                               2.249e+00  4.446e-01   0.27845   18.1643
ORdate_year2014                                               1.718e+00  5.821e-01   0.20304   14.5352
ORdate_year2015                                               9.863e-01  1.014e+00   0.08823   11.0245
ORdate_year2016                                               1.524e+00  6.561e-01   0.13624   17.0529
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.857e-01  1.129e+00   0.46388    1.6910
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.948e+00  5.134e-01   1.30344    2.9107
SmokerStatusEx-smoker                                         5.862e-01  1.706e+00   0.38253    0.8983
SmokerStatusNever smoked                                      4.562e-01  2.192e+00   0.24249    0.8581
Med.Statin.LLDno                                              1.534e+00  6.518e-01   0.98387    2.3926
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.020e+00  9.803e-01   0.58815    1.7693
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.889e-01  1.011e+00   0.97900    0.9989
BMI                                                           1.024e+00  9.766e-01   0.97356    1.0769
MedHx_CVDyes                                                  2.301e+00  4.345e-01   1.44781    3.6584
stenose0-49%                                                  1.968e-08  5.082e+07   0.00000       Inf
stenose50-70%                                                 1.925e-01  5.195e+00   0.05385    0.6880
stenose70-90%                                                 3.507e-01  2.852e+00   0.14590    0.8428
stenose90-99%                                                 3.121e-01  3.204e+00   0.12888    0.7556
stenose100% (Occlusion)                                       3.090e-08  3.237e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.241e-08  8.060e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.747  (se = 0.022 )
Likelihood ratio test= 94.39  on 33 df,   p=8e-08
Wald test            = 78.08  on 33 df,   p=2e-05
Score (logrank) test = 100.8  on 33 df,   p=9e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LAPTM5 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: LAPTM5 
Effect size...............: 0.357101 
Standard error............: 0.197656 
Odds ratio (effect size)..: 1.429 
Lower 95% CI..............: 0.97 
Upper 95% CI..............: 2.105 
T-value...................: 1.806682 
P-value...................: 0.07081192 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [MMP9]; 20 out of 35 target-of-interest.
   > cross tabulation of MMP9-stratum.

[2.37, 6.12) [6.12,12.12] 
         546          546 

   > fitting the model for MMP9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.12,12.12]  3.454e-01  1.413e+00  1.953e-01  1.769 0.076908 .  
Age                                                            4.080e-02  1.042e+00  1.330e-02  3.068 0.002156 ** 
Gendermale                                                     4.342e-01  1.544e+00  2.373e-01  1.830 0.067273 .  
ORdate_year2002                                                9.897e-01  2.690e+00  1.098e+00  0.901 0.367333    
ORdate_year2003                                                1.010e-02  1.010e+00  1.131e+00  0.009 0.992876    
ORdate_year2004                                                3.331e-01  1.395e+00  1.086e+00  0.307 0.759087    
ORdate_year2005                                                8.526e-01  2.346e+00  1.059e+00  0.805 0.420533    
ORdate_year2006                                                9.452e-01  2.573e+00  1.044e+00  0.906 0.365054    
ORdate_year2007                                                1.605e-01  1.174e+00  1.087e+00  0.148 0.882545    
ORdate_year2008                                                9.153e-01  2.498e+00  1.054e+00  0.868 0.385243    
ORdate_year2009                                                2.310e-01  1.260e+00  1.069e+00  0.216 0.828966    
ORdate_year2010                                                4.146e-01  1.514e+00  1.071e+00  0.387 0.698530    
ORdate_year2011                                                6.123e-01  1.845e+00  1.087e+00  0.563 0.573246    
ORdate_year2012                                                1.045e+00  2.844e+00  1.057e+00  0.989 0.322649    
ORdate_year2013                                                8.066e-01  2.240e+00  1.067e+00  0.756 0.449668    
ORdate_year2014                                                5.310e-01  1.701e+00  1.090e+00  0.487 0.626233    
ORdate_year2015                                               -8.922e-02  9.146e-01  1.232e+00 -0.072 0.942259    
ORdate_year2016                                                3.662e-01  1.442e+00  1.233e+00  0.297 0.766497    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.203e-01  8.866e-01  3.296e-01 -0.365 0.715067    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.603e-01  1.935e+00  2.045e-01  3.229 0.001241 ** 
SmokerStatusEx-smoker                                         -5.249e-01  5.916e-01  2.177e-01 -2.411 0.015925 *  
SmokerStatusNever smoked                                      -7.833e-01  4.569e-01  3.224e-01 -2.430 0.015118 *  
Med.Statin.LLDno                                               4.373e-01  1.549e+00  2.271e-01  1.925 0.054193 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         6.133e-02  1.063e+00  2.802e-01  0.219 0.826737    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.131e-02  9.888e-01  5.126e-03 -2.205 0.027425 *  
BMI                                                            2.361e-02  1.024e+00  2.578e-02  0.916 0.359672    
MedHx_CVDyes                                                   8.298e-01  2.293e+00  2.367e-01  3.506 0.000455 ***
stenose0-49%                                                  -1.777e+01  1.924e-08  4.170e+03 -0.004 0.996600    
stenose50-70%                                                 -1.660e+00  1.902e-01  6.505e-01 -2.551 0.010743 *  
stenose70-90%                                                 -1.076e+00  3.410e-01  4.494e-01 -2.394 0.016679 *  
stenose90-99%                                                 -1.195e+00  3.029e-01  4.531e-01 -2.636 0.008386 ** 
stenose100% (Occlusion)                                       -1.729e+01  3.086e-08  3.657e+03 -0.005 0.996227    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.820e+01  1.251e-08  6.132e+03 -0.003 0.997632    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.12,12.12] 1.413e+00  7.079e-01   0.96338    2.0712
Age                                                           1.042e+00  9.600e-01   1.01484    1.0692
Gendermale                                                    1.544e+00  6.478e-01   0.96959    2.4580
ORdate_year2002                                               2.690e+00  3.717e-01   0.31285   23.1359
ORdate_year2003                                               1.010e+00  9.899e-01   0.11001    9.2753
ORdate_year2004                                               1.395e+00  7.167e-01   0.16601   11.7269
ORdate_year2005                                               2.346e+00  4.263e-01   0.29463   18.6771
ORdate_year2006                                               2.573e+00  3.886e-01   0.33284   19.8970
ORdate_year2007                                               1.174e+00  8.517e-01   0.13959    9.8759
ORdate_year2008                                               2.498e+00  4.004e-01   0.31638   19.7164
ORdate_year2009                                               1.260e+00  7.937e-01   0.15491   10.2464
ORdate_year2010                                               1.514e+00  6.606e-01   0.18571   12.3395
ORdate_year2011                                               1.845e+00  5.421e-01   0.21909   15.5320
ORdate_year2012                                               2.844e+00  3.516e-01   0.35840   22.5679
ORdate_year2013                                               2.240e+00  4.464e-01   0.27673   18.1376
ORdate_year2014                                               1.701e+00  5.880e-01   0.20071   14.4098
ORdate_year2015                                               9.146e-01  1.093e+00   0.08179   10.2281
ORdate_year2016                                               1.442e+00  6.933e-01   0.12860   16.1756
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.866e-01  1.128e+00   0.46467    1.6917
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.935e+00  5.167e-01   1.29633    2.8893
SmokerStatusEx-smoker                                         5.916e-01  1.690e+00   0.38609    0.9065
SmokerStatusNever smoked                                      4.569e-01  2.189e+00   0.24286    0.8595
Med.Statin.LLDno                                              1.549e+00  6.458e-01   0.99215    2.4168
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.063e+00  9.405e-01   0.61397    1.8413
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.888e-01  1.011e+00   0.97887    0.9987
BMI                                                           1.024e+00  9.767e-01   0.97345    1.0769
MedHx_CVDyes                                                  2.293e+00  4.361e-01   1.44183    3.6461
stenose0-49%                                                  1.924e-08  5.199e+07   0.00000       Inf
stenose50-70%                                                 1.902e-01  5.257e+00   0.05315    0.6808
stenose70-90%                                                 3.410e-01  2.932e+00   0.14134    0.8229
stenose90-99%                                                 3.029e-01  3.302e+00   0.12460    0.7361
stenose100% (Occlusion)                                       3.086e-08  3.240e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.251e-08  7.996e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.748  (se = 0.021 )
Likelihood ratio test= 94.27  on 33 df,   p=8e-08
Wald test            = 78.05  on 33 df,   p=2e-05
Score (logrank) test = 100.5  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MMP9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: MMP9 
Effect size...............: 0.345401 
Standard error............: 0.195262 
Odds ratio (effect size)..: 1.413 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 2.071 
T-value...................: 1.768914 
P-value...................: 0.0769082 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [SIGLEC1]; 21 out of 35 target-of-interest.
   > cross tabulation of SIGLEC1-stratum.

[2.37,5.42) [5.42,9.88] 
        546         546 

   > fitting the model for SIGLEC1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.42,9.88]  3.397e-01  1.404e+00  1.992e-01  1.705  0.08811 .  
Age                                                           4.189e-02  1.043e+00  1.334e-02  3.140  0.00169 ** 
Gendermale                                                    4.474e-01  1.564e+00  2.372e-01  1.886  0.05928 .  
ORdate_year2002                                               1.083e+00  2.953e+00  1.099e+00  0.985  0.32450    
ORdate_year2003                                               5.872e-02  1.060e+00  1.132e+00  0.052  0.95861    
ORdate_year2004                                               3.952e-01  1.485e+00  1.086e+00  0.364  0.71607    
ORdate_year2005                                               9.276e-01  2.528e+00  1.058e+00  0.877  0.38062    
ORdate_year2006                                               1.002e+00  2.724e+00  1.044e+00  0.960  0.33698    
ORdate_year2007                                               2.124e-01  1.237e+00  1.088e+00  0.195  0.84518    
ORdate_year2008                                               9.758e-01  2.653e+00  1.054e+00  0.925  0.35475    
ORdate_year2009                                               3.207e-01  1.378e+00  1.071e+00  0.300  0.76447    
ORdate_year2010                                               4.940e-01  1.639e+00  1.070e+00  0.462  0.64438    
ORdate_year2011                                               6.716e-01  1.957e+00  1.087e+00  0.618  0.53659    
ORdate_year2012                                               1.131e+00  3.098e+00  1.058e+00  1.069  0.28515    
ORdate_year2013                                               8.508e-01  2.342e+00  1.065e+00  0.799  0.42434    
ORdate_year2014                                               5.624e-01  1.755e+00  1.090e+00  0.516  0.60589    
ORdate_year2015                                               1.443e-02  1.015e+00  1.233e+00  0.012  0.99066    
ORdate_year2016                                               4.330e-01  1.542e+00  1.233e+00  0.351  0.72538    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.204e-01  8.865e-01  3.297e-01 -0.365  0.71492    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.663e-01  1.947e+00  2.047e-01  3.256  0.00113 ** 
SmokerStatusEx-smoker                                        -5.442e-01  5.803e-01  2.184e-01 -2.492  0.01270 *  
SmokerStatusNever smoked                                     -7.691e-01  4.634e-01  3.220e-01 -2.389  0.01691 *  
Med.Statin.LLDno                                              4.280e-01  1.534e+00  2.270e-01  1.886  0.05932 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        1.998e-02  1.020e+00  2.808e-01  0.071  0.94328    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.134e-02  9.887e-01  5.085e-03 -2.230  0.02572 *  
BMI                                                           2.300e-02  1.023e+00  2.588e-02  0.889  0.37419    
MedHx_CVDyes                                                  8.380e-01  2.312e+00  2.362e-01  3.547  0.00039 ***
stenose0-49%                                                 -1.776e+01  1.942e-08  4.181e+03 -0.004  0.99661    
stenose50-70%                                                -1.637e+00  1.946e-01  6.509e-01 -2.515  0.01191 *  
stenose70-90%                                                -1.045e+00  3.517e-01  4.472e-01 -2.336  0.01947 *  
stenose90-99%                                                -1.184e+00  3.062e-01  4.517e-01 -2.621  0.00878 ** 
stenose100% (Occlusion)                                      -1.730e+01  3.068e-08  3.658e+03 -0.005  0.99623    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.821e+01  1.239e-08  6.130e+03 -0.003  0.99763    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.42,9.88] 1.404e+00  7.120e-01   0.95058    2.0751
Age                                                          1.043e+00  9.590e-01   1.01586    1.0704
Gendermale                                                   1.564e+00  6.393e-01   0.98264    2.4899
ORdate_year2002                                              2.953e+00  3.387e-01   0.34262   25.4475
ORdate_year2003                                              1.060e+00  9.430e-01   0.11544    9.7419
ORdate_year2004                                              1.485e+00  6.736e-01   0.17654   12.4851
ORdate_year2005                                              2.528e+00  3.955e-01   0.31789   20.1116
ORdate_year2006                                              2.724e+00  3.671e-01   0.35218   21.0742
ORdate_year2007                                              1.237e+00  8.087e-01   0.14671   10.4237
ORdate_year2008                                              2.653e+00  3.769e-01   0.33594   20.9544
ORdate_year2009                                              1.378e+00  7.256e-01   0.16907   11.2334
ORdate_year2010                                              1.639e+00  6.102e-01   0.20116   13.3522
ORdate_year2011                                              1.957e+00  5.109e-01   0.23260   16.4726
ORdate_year2012                                              3.098e+00  3.228e-01   0.38959   24.6281
ORdate_year2013                                              2.342e+00  4.271e-01   0.29042   18.8786
ORdate_year2014                                              1.755e+00  5.699e-01   0.20723   14.8606
ORdate_year2015                                              1.015e+00  9.857e-01   0.09059   11.3624
ORdate_year2016                                              1.542e+00  6.486e-01   0.13768   17.2660
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.865e-01  1.128e+00   0.46459    1.6918
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.947e+00  5.136e-01   1.30364    2.9079
SmokerStatusEx-smoker                                        5.803e-01  1.723e+00   0.37824    0.8903
SmokerStatusNever smoked                                     4.634e-01  2.158e+00   0.24657    0.8711
Med.Statin.LLDno                                             1.534e+00  6.518e-01   0.98331    2.3936
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.020e+00  9.802e-01   0.58833    1.7690
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.887e-01  1.011e+00   0.97892    0.9986
BMI                                                          1.023e+00  9.773e-01   0.97265    1.0765
MedHx_CVDyes                                                 2.312e+00  4.326e-01   1.45490    3.6729
stenose0-49%                                                 1.942e-08  5.149e+07   0.00000       Inf
stenose50-70%                                                1.946e-01  5.139e+00   0.05433    0.6969
stenose70-90%                                                3.517e-01  2.843e+00   0.14639    0.8451
stenose90-99%                                                3.062e-01  3.266e+00   0.12634    0.7420
stenose100% (Occlusion)                                      3.068e-08  3.259e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                1.239e-08  8.073e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.747  (se = 0.022 )
Likelihood ratio test= 94.03  on 33 df,   p=9e-08
Wald test            = 77.85  on 33 df,   p=2e-05
Score (logrank) test = 100.2  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' SIGLEC1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: SIGLEC1 
Effect size...............: 0.339666 
Standard error............: 0.199163 
Odds ratio (effect size)..: 1.404 
Lower 95% CI..............: 0.951 
Upper 95% CI..............: 2.075 
T-value...................: 1.705465 
P-value...................: 0.08810765 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [FTL]; 22 out of 35 target-of-interest.
   > cross tabulation of FTL-stratum.

[3.23, 9.66) [9.66,13.76] 
         546          546 

   > fitting the model for FTL-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][9.66,13.76]  1.581e-01  1.171e+00  1.965e-01  0.804 0.421131    
Age                                                            4.071e-02  1.042e+00  1.333e-02  3.053 0.002264 ** 
Gendermale                                                     4.481e-01  1.565e+00  2.373e-01  1.888 0.058995 .  
ORdate_year2002                                                9.974e-01  2.711e+00  1.099e+00  0.908 0.363991    
ORdate_year2003                                                1.272e-02  1.013e+00  1.132e+00  0.011 0.991030    
ORdate_year2004                                                3.512e-01  1.421e+00  1.087e+00  0.323 0.746739    
ORdate_year2005                                                8.724e-01  2.393e+00  1.059e+00  0.824 0.410002    
ORdate_year2006                                                9.491e-01  2.583e+00  1.044e+00  0.909 0.363519    
ORdate_year2007                                                1.230e-01  1.131e+00  1.087e+00  0.113 0.909875    
ORdate_year2008                                                9.245e-01  2.521e+00  1.055e+00  0.876 0.380805    
ORdate_year2009                                                2.345e-01  1.264e+00  1.070e+00  0.219 0.826531    
ORdate_year2010                                                4.392e-01  1.551e+00  1.071e+00  0.410 0.681788    
ORdate_year2011                                                6.391e-01  1.895e+00  1.087e+00  0.588 0.556671    
ORdate_year2012                                                1.067e+00  2.907e+00  1.058e+00  1.009 0.313143    
ORdate_year2013                                                8.588e-01  2.360e+00  1.067e+00  0.805 0.421101    
ORdate_year2014                                                5.756e-01  1.778e+00  1.091e+00  0.527 0.597873    
ORdate_year2015                                               -6.466e-02  9.374e-01  1.232e+00 -0.052 0.958140    
ORdate_year2016                                                4.202e-01  1.522e+00  1.233e+00  0.341 0.733276    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.217e-01  8.855e-01  3.300e-01 -0.369 0.712405    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.653e-01  1.945e+00  2.056e-01  3.235 0.001216 ** 
SmokerStatusEx-smoker                                         -5.197e-01  5.947e-01  2.180e-01 -2.383 0.017151 *  
SmokerStatusNever smoked                                      -7.680e-01  4.640e-01  3.227e-01 -2.380 0.017306 *  
Med.Statin.LLDno                                               4.134e-01  1.512e+00  2.266e-01  1.824 0.068118 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         3.871e-02  1.039e+00  2.801e-01  0.138 0.890071    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.107e-02  9.890e-01  5.075e-03 -2.180 0.029221 *  
BMI                                                            2.327e-02  1.024e+00  2.585e-02  0.900 0.368024    
MedHx_CVDyes                                                   8.178e-01  2.265e+00  2.360e-01  3.465 0.000529 ***
stenose0-49%                                                  -1.771e+01  2.031e-08  4.177e+03 -0.004 0.996617    
stenose50-70%                                                 -1.602e+00  2.014e-01  6.501e-01 -2.465 0.013709 *  
stenose70-90%                                                 -1.014e+00  3.628e-01  4.464e-01 -2.271 0.023120 *  
stenose90-99%                                                 -1.143e+00  3.190e-01  4.502e-01 -2.538 0.011162 *  
stenose100% (Occlusion)                                       -1.732e+01  3.008e-08  3.670e+03 -0.005 0.996235    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.819e+01  1.257e-08  6.135e+03 -0.003 0.997634    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][9.66,13.76] 1.171e+00  8.538e-01   0.79685    1.7217
Age                                                           1.042e+00  9.601e-01   1.01468    1.0691
Gendermale                                                    1.565e+00  6.388e-01   0.98312    2.4925
ORdate_year2002                                               2.711e+00  3.688e-01   0.31471   23.3586
ORdate_year2003                                               1.013e+00  9.874e-01   0.11017    9.3104
ORdate_year2004                                               1.421e+00  7.039e-01   0.16861   11.9716
ORdate_year2005                                               2.393e+00  4.180e-01   0.30031   19.0626
ORdate_year2006                                               2.583e+00  3.871e-01   0.33353   20.0096
ORdate_year2007                                               1.131e+00  8.842e-01   0.13437    9.5179
ORdate_year2008                                               2.521e+00  3.967e-01   0.31888   19.9231
ORdate_year2009                                               1.264e+00  7.909e-01   0.15522   10.2982
ORdate_year2010                                               1.551e+00  6.446e-01   0.19012   12.6596
ORdate_year2011                                               1.895e+00  5.278e-01   0.22493   15.9613
ORdate_year2012                                               2.907e+00  3.440e-01   0.36554   23.1133
ORdate_year2013                                               2.360e+00  4.237e-01   0.29130   19.1250
ORdate_year2014                                               1.778e+00  5.624e-01   0.20944   15.0977
ORdate_year2015                                               9.374e-01  1.067e+00   0.08382   10.4831
ORdate_year2016                                               1.522e+00  6.569e-01   0.13580   17.0645
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.855e-01  1.129e+00   0.46373    1.6907
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.945e+00  5.141e-01   1.29982    2.9104
SmokerStatusEx-smoker                                         5.947e-01  1.681e+00   0.38791    0.9118
SmokerStatusNever smoked                                      4.640e-01  2.155e+00   0.24651    0.8732
Med.Statin.LLDno                                              1.512e+00  6.614e-01   0.96971    2.3574
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.039e+00  9.620e-01   0.60037    1.7997
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.890e-01  1.011e+00   0.97921    0.9989
BMI                                                           1.024e+00  9.770e-01   0.97298    1.0767
MedHx_CVDyes                                                  2.265e+00  4.414e-01   1.42654    3.5977
stenose0-49%                                                  2.031e-08  4.924e+07   0.00000       Inf
stenose50-70%                                                 2.014e-01  4.964e+00   0.05634    0.7202
stenose70-90%                                                 3.628e-01  2.757e+00   0.15124    0.8702
stenose90-99%                                                 3.190e-01  3.135e+00   0.13200    0.7710
stenose100% (Occlusion)                                       3.008e-08  3.324e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.257e-08  7.954e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.747  (se = 0.021 )
Likelihood ratio test= 91.76  on 33 df,   p=2e-07
Wald test            = 76.62  on 33 df,   p=3e-05
Score (logrank) test = 98.48  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' FTL ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: FTL 
Effect size...............: 0.158104 
Standard error............: 0.196533 
Odds ratio (effect size)..: 1.171 
Lower 95% CI..............: 0.797 
Upper 95% CI..............: 1.722 
T-value...................: 0.804461 
P-value...................: 0.4211305 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [CD14]; 23 out of 35 target-of-interest.
   > cross tabulation of CD14-stratum.

[2.37, 6.73) [6.73,11.30] 
         546          546 

   > fitting the model for CD14-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.73,11.30]  4.776e-01  1.612e+00  1.996e-01  2.393 0.016705 *  
Age                                                            4.422e-02  1.045e+00  1.345e-02  3.288 0.001008 ** 
Gendermale                                                     4.320e-01  1.540e+00  2.378e-01  1.817 0.069224 .  
ORdate_year2002                                                1.123e+00  3.074e+00  1.098e+00  1.023 0.306355    
ORdate_year2003                                                4.578e-02  1.047e+00  1.131e+00  0.040 0.967708    
ORdate_year2004                                                3.946e-01  1.484e+00  1.086e+00  0.363 0.716274    
ORdate_year2005                                                9.419e-01  2.565e+00  1.057e+00  0.891 0.372845    
ORdate_year2006                                                1.034e+00  2.814e+00  1.044e+00  0.991 0.321522    
ORdate_year2007                                                2.578e-01  1.294e+00  1.087e+00  0.237 0.812565    
ORdate_year2008                                                1.003e+00  2.726e+00  1.054e+00  0.952 0.341207    
ORdate_year2009                                                3.450e-01  1.412e+00  1.070e+00  0.322 0.747099    
ORdate_year2010                                                4.886e-01  1.630e+00  1.070e+00  0.457 0.647773    
ORdate_year2011                                                7.118e-01  2.038e+00  1.087e+00  0.655 0.512500    
ORdate_year2012                                                1.145e+00  3.142e+00  1.057e+00  1.084 0.278538    
ORdate_year2013                                                8.396e-01  2.315e+00  1.065e+00  0.789 0.430294    
ORdate_year2014                                                5.560e-01  1.744e+00  1.089e+00  0.510 0.609791    
ORdate_year2015                                                3.310e-02  1.034e+00  1.232e+00  0.027 0.978570    
ORdate_year2016                                                4.199e-01  1.522e+00  1.232e+00  0.341 0.733318    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.304e-01  8.778e-01  3.300e-01 -0.395 0.692759    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.651e-01  1.945e+00  2.053e-01  3.239 0.001200 ** 
SmokerStatusEx-smoker                                         -5.468e-01  5.788e-01  2.181e-01 -2.508 0.012151 *  
SmokerStatusNever smoked                                      -8.033e-01  4.479e-01  3.228e-01 -2.488 0.012834 *  
Med.Statin.LLDno                                               4.471e-01  1.564e+00  2.268e-01  1.971 0.048669 *  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         8.140e-03  1.008e+00  2.812e-01  0.029 0.976904    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.136e-02  9.887e-01  5.158e-03 -2.203 0.027574 *  
BMI                                                            2.544e-02  1.026e+00  2.584e-02  0.985 0.324765    
MedHx_CVDyes                                                   8.343e-01  2.303e+00  2.363e-01  3.531 0.000414 ***
stenose0-49%                                                  -1.776e+01  1.931e-08  4.112e+03 -0.004 0.996553    
stenose50-70%                                                 -1.664e+00  1.894e-01  6.521e-01 -2.552 0.010716 *  
stenose70-90%                                                 -1.064e+00  3.450e-01  4.485e-01 -2.373 0.017662 *  
stenose90-99%                                                 -1.179e+00  3.076e-01  4.527e-01 -2.604 0.009212 ** 
stenose100% (Occlusion)                                       -1.726e+01  3.188e-08  3.632e+03 -0.005 0.996208    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.819e+01  1.265e-08  6.112e+03 -0.003 0.997626    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.73,11.30] 1.612e+00  6.203e-01   1.09030    2.3841
Age                                                           1.045e+00  9.567e-01   1.01802    1.0731
Gendermale                                                    1.540e+00  6.492e-01   0.96657    2.4549
ORdate_year2002                                               3.074e+00  3.253e-01   0.35743   26.4411
ORdate_year2003                                               1.047e+00  9.553e-01   0.11413    9.6022
ORdate_year2004                                               1.484e+00  6.739e-01   0.17665   12.4644
ORdate_year2005                                               2.565e+00  3.899e-01   0.32316   20.3553
ORdate_year2006                                               2.814e+00  3.554e-01   0.36393   21.7539
ORdate_year2007                                               1.294e+00  7.728e-01   0.15368   10.8967
ORdate_year2008                                               2.726e+00  3.669e-01   0.34570   21.4931
ORdate_year2009                                               1.412e+00  7.082e-01   0.17347   11.4920
ORdate_year2010                                               1.630e+00  6.135e-01   0.20035   13.2628
ORdate_year2011                                               2.038e+00  4.908e-01   0.24215   17.1456
ORdate_year2012                                               3.142e+00  3.182e-01   0.39613   24.9286
ORdate_year2013                                               2.315e+00  4.319e-01   0.28741   18.6521
ORdate_year2014                                               1.744e+00  5.735e-01   0.20617   14.7458
ORdate_year2015                                               1.034e+00  9.674e-01   0.09236   11.5686
ORdate_year2016                                               1.522e+00  6.571e-01   0.13595   17.0350
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.778e-01  1.139e+00   0.45969    1.6760
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.945e+00  5.142e-01   1.30033    2.9081
SmokerStatusEx-smoker                                         5.788e-01  1.728e+00   0.37749    0.8874
SmokerStatusNever smoked                                      4.479e-01  2.233e+00   0.23788    0.8432
Med.Statin.LLDno                                              1.564e+00  6.395e-01   1.00261    2.4388
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.008e+00  9.919e-01   0.58104    1.7493
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.887e-01  1.011e+00   0.97875    0.9987
BMI                                                           1.026e+00  9.749e-01   0.97512    1.0790
MedHx_CVDyes                                                  2.303e+00  4.342e-01   1.44945    3.6597
stenose0-49%                                                  1.931e-08  5.178e+07   0.00000       Inf
stenose50-70%                                                 1.894e-01  5.281e+00   0.05276    0.6798
stenose70-90%                                                 3.450e-01  2.898e+00   0.14324    0.8310
stenose90-99%                                                 3.076e-01  3.251e+00   0.12667    0.7471
stenose100% (Occlusion)                                       3.188e-08  3.137e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.265e-08  7.903e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.748  (se = 0.022 )
Likelihood ratio test= 96.89  on 33 df,   p=3e-08
Wald test            = 79.96  on 33 df,   p=9e-06
Score (logrank) test = 103  on 33 df,   p=4e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD14 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: CD14 
Effect size...............: 0.47763 
Standard error............: 0.199584 
Odds ratio (effect size)..: 1.612 
Lower 95% CI..............: 1.09 
Upper 95% CI..............: 2.384 
T-value...................: 2.393126 
P-value...................: 0.0167055 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [HCST]; 24 out of 35 target-of-interest.
   > cross tabulation of HCST-stratum.

[2.37, 7.02) [7.02,11.97] 
         546          546 

   > fitting the model for HCST-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.02,11.97]  3.476e-01  1.416e+00  1.991e-01  1.746 0.080880 .  
Age                                                            4.253e-02  1.043e+00  1.340e-02  3.174 0.001505 ** 
Gendermale                                                     4.388e-01  1.551e+00  2.376e-01  1.846 0.064841 .  
ORdate_year2002                                                1.045e+00  2.843e+00  1.098e+00  0.951 0.341431    
ORdate_year2003                                                7.580e-03  1.008e+00  1.131e+00  0.007 0.994655    
ORdate_year2004                                                3.556e-01  1.427e+00  1.087e+00  0.327 0.743527    
ORdate_year2005                                                8.921e-01  2.440e+00  1.058e+00  0.843 0.399031    
ORdate_year2006                                                9.807e-01  2.666e+00  1.044e+00  0.939 0.347507    
ORdate_year2007                                                1.824e-01  1.200e+00  1.087e+00  0.168 0.866703    
ORdate_year2008                                                9.575e-01  2.605e+00  1.054e+00  0.908 0.363795    
ORdate_year2009                                                2.903e-01  1.337e+00  1.070e+00  0.271 0.786137    
ORdate_year2010                                                4.477e-01  1.565e+00  1.070e+00  0.418 0.675754    
ORdate_year2011                                                6.757e-01  1.965e+00  1.087e+00  0.621 0.534313    
ORdate_year2012                                                1.106e+00  3.023e+00  1.058e+00  1.046 0.295614    
ORdate_year2013                                                8.320e-01  2.298e+00  1.066e+00  0.781 0.434920    
ORdate_year2014                                                5.388e-01  1.714e+00  1.091e+00  0.494 0.621296    
ORdate_year2015                                               -1.197e-02  9.881e-01  1.232e+00 -0.010 0.992247    
ORdate_year2016                                                4.119e-01  1.510e+00  1.233e+00  0.334 0.738332    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.341e-01  8.745e-01  3.301e-01 -0.406 0.684593    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.716e-01  1.957e+00  2.054e-01  3.270 0.001077 ** 
SmokerStatusEx-smoker                                         -5.465e-01  5.790e-01  2.184e-01 -2.502 0.012342 *  
SmokerStatusNever smoked                                      -7.787e-01  4.590e-01  3.223e-01 -2.416 0.015695 *  
Med.Statin.LLDno                                               4.358e-01  1.546e+00  2.268e-01  1.921 0.054689 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         5.241e-03  1.005e+00  2.814e-01  0.019 0.985140    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.111e-02  9.890e-01  5.127e-03 -2.167 0.030256 *  
BMI                                                            2.349e-02  1.024e+00  2.585e-02  0.908 0.363620    
MedHx_CVDyes                                                   8.358e-01  2.307e+00  2.364e-01  3.535 0.000407 ***
stenose0-49%                                                  -1.774e+01  1.971e-08  4.148e+03 -0.004 0.996587    
stenose50-70%                                                 -1.642e+00  1.936e-01  6.510e-01 -2.523 0.011651 *  
stenose70-90%                                                 -1.038e+00  3.542e-01  4.472e-01 -2.320 0.020316 *  
stenose90-99%                                                 -1.160e+00  3.134e-01  4.510e-01 -2.573 0.010095 *  
stenose100% (Occlusion)                                       -1.724e+01  3.242e-08  3.643e+03 -0.005 0.996223    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.821e+01  1.240e-08  6.135e+03 -0.003 0.997632    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.02,11.97] 1.416e+00  7.064e-01   0.95821    2.0917
Age                                                           1.043e+00  9.584e-01   1.01640    1.0712
Gendermale                                                    1.551e+00  6.448e-01   0.97336    2.4707
ORdate_year2002                                               2.843e+00  3.517e-01   0.33030   24.4696
ORdate_year2003                                               1.008e+00  9.924e-01   0.10970    9.2551
ORdate_year2004                                               1.427e+00  7.008e-01   0.16955   12.0105
ORdate_year2005                                               2.440e+00  4.098e-01   0.30694   19.3996
ORdate_year2006                                               2.666e+00  3.751e-01   0.34462   20.6282
ORdate_year2007                                               1.200e+00  8.333e-01   0.14261   10.0993
ORdate_year2008                                               2.605e+00  3.838e-01   0.32989   20.5752
ORdate_year2009                                               1.337e+00  7.480e-01   0.16415   10.8878
ORdate_year2010                                               1.565e+00  6.391e-01   0.19200   12.7523
ORdate_year2011                                               1.965e+00  5.088e-01   0.23332   16.5547
ORdate_year2012                                               3.023e+00  3.308e-01   0.38027   24.0335
ORdate_year2013                                               2.298e+00  4.352e-01   0.28463   18.5532
ORdate_year2014                                               1.714e+00  5.835e-01   0.20215   14.5307
ORdate_year2015                                               9.881e-01  1.012e+00   0.08828   11.0589
ORdate_year2016                                               1.510e+00  6.624e-01   0.13471   16.9173
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.745e-01  1.143e+00   0.45795    1.6700
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.957e+00  5.109e-01   1.30868    2.9275
SmokerStatusEx-smoker                                         5.790e-01  1.727e+00   0.37735    0.8883
SmokerStatusNever smoked                                      4.590e-01  2.179e+00   0.24404    0.8633
Med.Statin.LLDno                                              1.546e+00  6.468e-01   0.99128    2.4116
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.005e+00  9.948e-01   0.57910    1.7450
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.890e-01  1.011e+00   0.97906    0.9989
BMI                                                           1.024e+00  9.768e-01   0.97318    1.0770
MedHx_CVDyes                                                  2.307e+00  4.335e-01   1.45124    3.6661
stenose0-49%                                                  1.971e-08  5.074e+07   0.00000       Inf
stenose50-70%                                                 1.936e-01  5.167e+00   0.05403    0.6933
stenose70-90%                                                 3.542e-01  2.823e+00   0.14744    0.8511
stenose90-99%                                                 3.134e-01  3.191e+00   0.12946    0.7586
stenose100% (Occlusion)                                       3.242e-08  3.084e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.240e-08  8.064e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.747  (se = 0.022 )
Likelihood ratio test= 94.17  on 33 df,   p=9e-08
Wald test            = 78.05  on 33 df,   p=2e-05
Score (logrank) test = 100.8  on 33 df,   p=9e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' HCST ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: HCST 
Effect size...............: 0.347635 
Standard error............: 0.199149 
Odds ratio (effect size)..: 1.416 
Lower 95% CI..............: 0.958 
Upper 95% CI..............: 2.092 
T-value...................: 1.745602 
P-value...................: 0.08088011 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [TIMP3]; 25 out of 35 target-of-interest.
   > cross tabulation of TIMP3-stratum.

[2.37, 6.14) [6.14,11.90] 
         546          546 

   > fitting the model for TIMP3-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.14,11.90]  2.366e-01  1.267e+00  2.026e-01  1.168 0.242771    
Age                                                            4.013e-02  1.041e+00  1.330e-02  3.018 0.002548 ** 
Gendermale                                                     4.834e-01  1.622e+00  2.383e-01  2.028 0.042537 *  
ORdate_year2002                                                1.079e+00  2.942e+00  1.101e+00  0.980 0.327274    
ORdate_year2003                                                1.079e-01  1.114e+00  1.135e+00  0.095 0.924263    
ORdate_year2004                                                4.316e-01  1.540e+00  1.089e+00  0.396 0.691848    
ORdate_year2005                                                9.641e-01  2.622e+00  1.060e+00  0.909 0.363233    
ORdate_year2006                                                1.042e+00  2.836e+00  1.046e+00  0.996 0.319181    
ORdate_year2007                                                2.157e-01  1.241e+00  1.091e+00  0.198 0.843278    
ORdate_year2008                                                1.015e+00  2.759e+00  1.057e+00  0.960 0.337003    
ORdate_year2009                                                3.105e-01  1.364e+00  1.072e+00  0.290 0.772109    
ORdate_year2010                                                4.981e-01  1.646e+00  1.071e+00  0.465 0.642032    
ORdate_year2011                                                6.880e-01  1.990e+00  1.088e+00  0.633 0.527013    
ORdate_year2012                                                1.068e+00  2.908e+00  1.058e+00  1.010 0.312719    
ORdate_year2013                                                8.866e-01  2.427e+00  1.065e+00  0.833 0.404974    
ORdate_year2014                                                5.921e-01  1.808e+00  1.090e+00  0.543 0.586981    
ORdate_year2015                                                1.619e-02  1.016e+00  1.234e+00  0.013 0.989528    
ORdate_year2016                                                4.214e-01  1.524e+00  1.233e+00  0.342 0.732417    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.043e-01  9.010e-01  3.299e-01 -0.316 0.751914    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.629e-01  1.940e+00  2.048e-01  3.236 0.001211 ** 
SmokerStatusEx-smoker                                         -5.080e-01  6.017e-01  2.177e-01 -2.334 0.019591 *  
SmokerStatusNever smoked                                      -7.374e-01  4.784e-01  3.225e-01 -2.287 0.022221 *  
Med.Statin.LLDno                                               4.203e-01  1.522e+00  2.274e-01  1.848 0.064615 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         5.387e-02  1.055e+00  2.803e-01  0.192 0.847582    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.122e-02  9.888e-01  5.079e-03 -2.209 0.027202 *  
BMI                                                            2.293e-02  1.023e+00  2.593e-02  0.884 0.376614    
MedHx_CVDyes                                                   8.332e-01  2.301e+00  2.364e-01  3.524 0.000425 ***
stenose0-49%                                                  -1.674e+01  5.382e-08  2.562e+03 -0.007 0.994788    
stenose50-70%                                                 -1.602e+00  2.014e-01  6.496e-01 -2.467 0.013643 *  
stenose70-90%                                                 -1.022e+00  3.599e-01  4.469e-01 -2.287 0.022204 *  
stenose90-99%                                                 -1.151e+00  3.164e-01  4.503e-01 -2.556 0.010598 *  
stenose100% (Occlusion)                                       -1.630e+01  8.306e-08  2.249e+03 -0.007 0.994217    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.720e+01  3.401e-08  3.778e+03 -0.005 0.996368    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.14,11.90] 1.267e+00  7.893e-01   0.85179    1.8845
Age                                                           1.041e+00  9.607e-01   1.01416    1.0684
Gendermale                                                    1.622e+00  6.167e-01   1.01640    2.5869
ORdate_year2002                                               2.942e+00  3.399e-01   0.33967   25.4763
ORdate_year2003                                               1.114e+00  8.978e-01   0.12054   10.2932
ORdate_year2004                                               1.540e+00  6.495e-01   0.18219   13.0122
ORdate_year2005                                               2.622e+00  3.813e-01   0.32819   20.9540
ORdate_year2006                                               2.836e+00  3.526e-01   0.36476   22.0467
ORdate_year2007                                               1.241e+00  8.060e-01   0.14625   10.5257
ORdate_year2008                                               2.759e+00  3.624e-01   0.34749   21.9121
ORdate_year2009                                               1.364e+00  7.331e-01   0.16687   11.1502
ORdate_year2010                                               1.646e+00  6.077e-01   0.20150   13.4387
ORdate_year2011                                               1.990e+00  5.026e-01   0.23603   16.7746
ORdate_year2012                                               2.908e+00  3.438e-01   0.36600   23.1120
ORdate_year2013                                               2.427e+00  4.120e-01   0.30117   19.5572
ORdate_year2014                                               1.808e+00  5.532e-01   0.21347   15.3089
ORdate_year2015                                               1.016e+00  9.839e-01   0.09055   11.4068
ORdate_year2016                                               1.524e+00  6.561e-01   0.13609   17.0698
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      9.010e-01  1.110e+00   0.47190    1.7201
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.940e+00  5.154e-01   1.29877    2.8990
SmokerStatusEx-smoker                                         6.017e-01  1.662e+00   0.39272    0.9218
SmokerStatusNever smoked                                      4.784e-01  2.090e+00   0.25424    0.9000
Med.Statin.LLDno                                              1.522e+00  6.568e-01   0.97484    2.3776
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.055e+00  9.476e-01   0.60931    1.8279
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.888e-01  1.011e+00   0.97905    0.9987
BMI                                                           1.023e+00  9.773e-01   0.97249    1.0765
MedHx_CVDyes                                                  2.301e+00  4.346e-01   1.44752    3.6568
stenose0-49%                                                  5.382e-08  1.858e+07   0.00000       Inf
stenose50-70%                                                 2.014e-01  4.965e+00   0.05638    0.7196
stenose70-90%                                                 3.599e-01  2.779e+00   0.14987    0.8641
stenose90-99%                                                 3.164e-01  3.161e+00   0.13090    0.7647
stenose100% (Occlusion)                                       8.306e-08  1.204e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.401e-08  2.940e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.75  (se = 0.021 )
Likelihood ratio test= 92.48  on 33 df,   p=2e-07
Wald test            = 76.73  on 33 df,   p=2e-05
Score (logrank) test = 99.01  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' TIMP3 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: TIMP3 
Effect size...............: 0.236626 
Standard error............: 0.202575 
Odds ratio (effect size)..: 1.267 
Lower 95% CI..............: 0.852 
Upper 95% CI..............: 1.885 
T-value...................: 1.168088 
P-value...................: 0.2427713 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [CCL2]; 26 out of 35 target-of-interest.
   > cross tabulation of CCL2-stratum.

[2.37, 6.27) [6.27,10.61] 
         546          546 

   > fitting the model for CCL2-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.27,10.61]  1.812e-01  1.199e+00  1.991e-01  0.910 0.362623    
Age                                                            4.054e-02  1.041e+00  1.331e-02  3.045 0.002328 ** 
Gendermale                                                     4.566e-01  1.579e+00  2.369e-01  1.928 0.053893 .  
ORdate_year2002                                                1.044e+00  2.840e+00  1.101e+00  0.948 0.343049    
ORdate_year2003                                                4.101e-02  1.042e+00  1.132e+00  0.036 0.971106    
ORdate_year2004                                                3.706e-01  1.449e+00  1.088e+00  0.341 0.733270    
ORdate_year2005                                                9.026e-01  2.466e+00  1.059e+00  0.853 0.393846    
ORdate_year2006                                                1.003e+00  2.726e+00  1.046e+00  0.959 0.337397    
ORdate_year2007                                                1.701e-01  1.185e+00  1.089e+00  0.156 0.875946    
ORdate_year2008                                                9.725e-01  2.645e+00  1.056e+00  0.921 0.357063    
ORdate_year2009                                                2.848e-01  1.329e+00  1.072e+00  0.266 0.790430    
ORdate_year2010                                                4.831e-01  1.621e+00  1.072e+00  0.451 0.652091    
ORdate_year2011                                                6.678e-01  1.950e+00  1.088e+00  0.614 0.539178    
ORdate_year2012                                                1.073e+00  2.925e+00  1.057e+00  1.015 0.310091    
ORdate_year2013                                                8.711e-01  2.390e+00  1.066e+00  0.817 0.413903    
ORdate_year2014                                                6.014e-01  1.825e+00  1.090e+00  0.552 0.581205    
ORdate_year2015                                               -2.477e-02  9.755e-01  1.233e+00 -0.020 0.983967    
ORdate_year2016                                                4.240e-01  1.528e+00  1.233e+00  0.344 0.730901    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.230e-01  8.842e-01  3.300e-01 -0.373 0.709236    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.581e-01  1.931e+00  2.051e-01  3.209 0.001334 ** 
SmokerStatusEx-smoker                                         -5.201e-01  5.944e-01  2.179e-01 -2.387 0.016995 *  
SmokerStatusNever smoked                                      -7.595e-01  4.679e-01  3.223e-01 -2.356 0.018457 *  
Med.Statin.LLDno                                               4.231e-01  1.527e+00  2.274e-01  1.861 0.062803 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         3.779e-02  1.039e+00  2.802e-01  0.135 0.892719    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.113e-02  9.889e-01  5.082e-03 -2.190 0.028542 *  
BMI                                                            2.266e-02  1.023e+00  2.588e-02  0.876 0.381277    
MedHx_CVDyes                                                   8.234e-01  2.278e+00  2.361e-01  3.487 0.000489 ***
stenose0-49%                                                  -1.773e+01  1.991e-08  4.201e+03 -0.004 0.996632    
stenose50-70%                                                 -1.607e+00  2.006e-01  6.506e-01 -2.469 0.013543 *  
stenose70-90%                                                 -1.041e+00  3.531e-01  4.492e-01 -2.318 0.020459 *  
stenose90-99%                                                 -1.169e+00  3.106e-01  4.522e-01 -2.586 0.009711 ** 
stenose100% (Occlusion)                                       -1.735e+01  2.931e-08  3.664e+03 -0.005 0.996223    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.828e+01  1.151e-08  6.103e+03 -0.003 0.997610    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.27,10.61] 1.199e+00  8.342e-01   0.81143    1.7708
Age                                                           1.041e+00  9.603e-01   1.01455    1.0689
Gendermale                                                    1.579e+00  6.334e-01   0.99239    2.5115
ORdate_year2002                                               2.840e+00  3.522e-01   0.32834   24.5578
ORdate_year2003                                               1.042e+00  9.598e-01   0.11325    9.5848
ORdate_year2004                                               1.449e+00  6.903e-01   0.17188   12.2096
ORdate_year2005                                               2.466e+00  4.055e-01   0.30970   19.6364
ORdate_year2006                                               2.726e+00  3.668e-01   0.35128   21.1608
ORdate_year2007                                               1.185e+00  8.436e-01   0.14012   10.0280
ORdate_year2008                                               2.645e+00  3.781e-01   0.33384   20.9486
ORdate_year2009                                               1.329e+00  7.522e-01   0.16274   10.8613
ORdate_year2010                                               1.621e+00  6.169e-01   0.19847   13.2415
ORdate_year2011                                               1.950e+00  5.128e-01   0.23138   16.4334
ORdate_year2012                                               2.925e+00  3.419e-01   0.36820   23.2339
ORdate_year2013                                               2.390e+00  4.185e-01   0.29565   19.3137
ORdate_year2014                                               1.825e+00  5.480e-01   0.21534   15.4624
ORdate_year2015                                               9.755e-01  1.025e+00   0.08711   10.9252
ORdate_year2016                                               1.528e+00  6.544e-01   0.13637   17.1238
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.842e-01  1.131e+00   0.46310    1.6883
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.931e+00  5.178e-01   1.29186    2.8866
SmokerStatusEx-smoker                                         5.944e-01  1.682e+00   0.38780    0.9112
SmokerStatusNever smoked                                      4.679e-01  2.137e+00   0.24877    0.8801
Med.Statin.LLDno                                              1.527e+00  6.550e-01   0.97765    2.3842
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.039e+00  9.629e-01   0.59964    1.7986
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.889e-01  1.011e+00   0.97913    0.9988
BMI                                                           1.023e+00  9.776e-01   0.97232    1.0761
MedHx_CVDyes                                                  2.278e+00  4.389e-01   1.43414    3.6191
stenose0-49%                                                  1.991e-08  5.022e+07   0.00000       Inf
stenose50-70%                                                 2.006e-01  4.985e+00   0.05604    0.7180
stenose70-90%                                                 3.531e-01  2.832e+00   0.14640    0.8515
stenose90-99%                                                 3.106e-01  3.220e+00   0.12802    0.7535
stenose100% (Occlusion)                                       2.931e-08  3.412e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.151e-08  8.689e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.747  (se = 0.021 )
Likelihood ratio test= 91.94  on 33 df,   p=2e-07
Wald test            = 76.54  on 33 df,   p=3e-05
Score (logrank) test = 98.39  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CCL2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: CCL2 
Effect size...............: 0.181248 
Standard error............: 0.19909 
Odds ratio (effect size)..: 1.199 
Lower 95% CI..............: 0.811 
Upper 95% CI..............: 1.771 
T-value...................: 0.910379 
P-value...................: 0.3626229 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [SAT1]; 27 out of 35 target-of-interest.
   > cross tabulation of SAT1-stratum.

[2.37, 7.73) [7.73,12.14] 
         546          546 

   > fitting the model for SAT1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.73,12.14]  2.005e-01  1.222e+00  1.984e-01  1.011 0.312199    
Age                                                            4.067e-02  1.042e+00  1.331e-02  3.056 0.002243 ** 
Gendermale                                                     4.531e-01  1.573e+00  2.369e-01  1.913 0.055764 .  
ORdate_year2002                                                1.040e+00  2.828e+00  1.100e+00  0.945 0.344577    
ORdate_year2003                                                3.733e-02  1.038e+00  1.132e+00  0.033 0.973691    
ORdate_year2004                                                3.801e-01  1.462e+00  1.087e+00  0.350 0.726659    
ORdate_year2005                                                9.081e-01  2.480e+00  1.059e+00  0.858 0.390973    
ORdate_year2006                                                9.717e-01  2.642e+00  1.044e+00  0.931 0.352102    
ORdate_year2007                                                1.725e-01  1.188e+00  1.089e+00  0.158 0.874153    
ORdate_year2008                                                9.574e-01  2.605e+00  1.055e+00  0.907 0.364239    
ORdate_year2009                                                2.741e-01  1.315e+00  1.071e+00  0.256 0.797966    
ORdate_year2010                                                4.718e-01  1.603e+00  1.071e+00  0.440 0.659609    
ORdate_year2011                                                6.811e-01  1.976e+00  1.088e+00  0.626 0.531327    
ORdate_year2012                                                1.079e+00  2.941e+00  1.058e+00  1.020 0.307772    
ORdate_year2013                                                8.612e-01  2.366e+00  1.066e+00  0.808 0.419204    
ORdate_year2014                                                5.927e-01  1.809e+00  1.091e+00  0.543 0.586796    
ORdate_year2015                                               -3.163e-02  9.689e-01  1.232e+00 -0.026 0.979523    
ORdate_year2016                                                4.125e-01  1.511e+00  1.233e+00  0.335 0.737960    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.250e-01  8.825e-01  3.297e-01 -0.379 0.704688    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.530e-01  1.921e+00  2.046e-01  3.191 0.001417 ** 
SmokerStatusEx-smoker                                         -5.227e-01  5.929e-01  2.176e-01 -2.402 0.016301 *  
SmokerStatusNever smoked                                      -7.690e-01  4.635e-01  3.224e-01 -2.385 0.017076 *  
Med.Statin.LLDno                                               4.162e-01  1.516e+00  2.267e-01  1.836 0.066400 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         3.604e-02  1.037e+00  2.803e-01  0.129 0.897707    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.122e-02  9.888e-01  5.081e-03 -2.208 0.027229 *  
BMI                                                            2.335e-02  1.024e+00  2.583e-02  0.904 0.365956    
MedHx_CVDyes                                                   8.241e-01  2.280e+00  2.361e-01  3.490 0.000484 ***
stenose0-49%                                                  -1.774e+01  1.969e-08  4.193e+03 -0.004 0.996624    
stenose50-70%                                                 -1.620e+00  1.979e-01  6.504e-01 -2.491 0.012755 *  
stenose70-90%                                                 -1.049e+00  3.503e-01  4.491e-01 -2.336 0.019500 *  
stenose90-99%                                                 -1.172e+00  3.099e-01  4.517e-01 -2.593 0.009501 ** 
stenose100% (Occlusion)                                       -1.734e+01  2.934e-08  3.669e+03 -0.005 0.996228    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.825e+01  1.186e-08  6.183e+03 -0.003 0.997645    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][7.73,12.14] 1.222e+00  8.183e-01   0.82833    1.8028
Age                                                           1.042e+00  9.601e-01   1.01470    1.0690
Gendermale                                                    1.573e+00  6.357e-01   0.98891    2.5026
ORdate_year2002                                               2.828e+00  3.536e-01   0.32750   24.4244
ORdate_year2003                                               1.038e+00  9.634e-01   0.11290    9.5436
ORdate_year2004                                               1.462e+00  6.838e-01   0.17357   12.3232
ORdate_year2005                                               2.480e+00  4.033e-01   0.31140   19.7444
ORdate_year2006                                               2.642e+00  3.785e-01   0.34131   20.4562
ORdate_year2007                                               1.188e+00  8.416e-01   0.14060   10.0422
ORdate_year2008                                               2.605e+00  3.839e-01   0.32932   20.6041
ORdate_year2009                                               1.315e+00  7.602e-01   0.16127   10.7282
ORdate_year2010                                               1.603e+00  6.239e-01   0.19638   13.0829
ORdate_year2011                                               1.976e+00  5.061e-01   0.23425   16.6681
ORdate_year2012                                               2.941e+00  3.401e-01   0.37002   23.3702
ORdate_year2013                                               2.366e+00  4.227e-01   0.29278   19.1199
ORdate_year2014                                               1.809e+00  5.528e-01   0.21336   15.3357
ORdate_year2015                                               9.689e-01  1.032e+00   0.08653   10.8482
ORdate_year2016                                               1.511e+00  6.620e-01   0.13476   16.9339
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.825e-01  1.133e+00   0.46244    1.6842
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.921e+00  5.205e-01   1.28652    2.8692
SmokerStatusEx-smoker                                         5.929e-01  1.687e+00   0.38707    0.9083
SmokerStatusNever smoked                                      4.635e-01  2.158e+00   0.24637    0.8719
Med.Statin.LLDno                                              1.516e+00  6.596e-01   0.97222    2.3644
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.037e+00  9.646e-01   0.59845    1.7959
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.888e-01  1.011e+00   0.97904    0.9987
BMI                                                           1.024e+00  9.769e-01   0.97310    1.0768
MedHx_CVDyes                                                  2.280e+00  4.386e-01   1.43509    3.6216
stenose0-49%                                                  1.969e-08  5.078e+07   0.00000       Inf
stenose50-70%                                                 1.979e-01  5.052e+00   0.05532    0.7082
stenose70-90%                                                 3.503e-01  2.855e+00   0.14524    0.8447
stenose90-99%                                                 3.099e-01  3.227e+00   0.12784    0.7511
stenose100% (Occlusion)                                       2.934e-08  3.408e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.186e-08  8.431e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.747  (se = 0.022 )
Likelihood ratio test= 92.13  on 33 df,   p=2e-07
Wald test            = 76.78  on 33 df,   p=2e-05
Score (logrank) test = 98.8  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' SAT1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: SAT1 
Effect size...............: 0.200495 
Standard error............: 0.198388 
Odds ratio (effect size)..: 1.222 
Lower 95% CI..............: 0.828 
Upper 95% CI..............: 1.803 
T-value...................: 1.010618 
P-value...................: 0.3121991 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [CD163]; 28 out of 35 target-of-interest.
   > cross tabulation of CD163-stratum.

[2.37, 5.74) [5.74,10.50] 
         546          546 

   > fitting the model for CD163-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.74,10.50]  2.236e-01  1.251e+00  1.954e-01  1.144 0.252513    
Age                                                            4.070e-02  1.042e+00  1.330e-02  3.060 0.002217 ** 
Gendermale                                                     4.433e-01  1.558e+00  2.370e-01  1.870 0.061447 .  
ORdate_year2002                                                1.037e+00  2.821e+00  1.099e+00  0.944 0.345329    
ORdate_year2003                                                5.015e-02  1.051e+00  1.132e+00  0.044 0.964662    
ORdate_year2004                                                3.947e-01  1.484e+00  1.087e+00  0.363 0.716575    
ORdate_year2005                                                9.136e-01  2.493e+00  1.058e+00  0.863 0.388020    
ORdate_year2006                                                1.002e+00  2.724e+00  1.045e+00  0.959 0.337375    
ORdate_year2007                                                1.833e-01  1.201e+00  1.089e+00  0.168 0.866256    
ORdate_year2008                                                9.706e-01  2.640e+00  1.055e+00  0.920 0.357603    
ORdate_year2009                                                2.793e-01  1.322e+00  1.071e+00  0.261 0.794217    
ORdate_year2010                                                4.785e-01  1.614e+00  1.071e+00  0.447 0.654972    
ORdate_year2011                                                6.853e-01  1.984e+00  1.088e+00  0.630 0.528700    
ORdate_year2012                                                1.081e+00  2.947e+00  1.057e+00  1.022 0.306694    
ORdate_year2013                                                8.781e-01  2.406e+00  1.065e+00  0.824 0.409780    
ORdate_year2014                                                5.846e-01  1.794e+00  1.090e+00  0.536 0.591816    
ORdate_year2015                                               -2.415e-02  9.761e-01  1.232e+00 -0.020 0.984364    
ORdate_year2016                                                3.995e-01  1.491e+00  1.233e+00  0.324 0.745892    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.239e-01  8.835e-01  3.301e-01 -0.375 0.707533    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.479e-01  1.912e+00  2.046e-01  3.167 0.001542 ** 
SmokerStatusEx-smoker                                         -5.202e-01  5.944e-01  2.176e-01 -2.391 0.016804 *  
SmokerStatusNever smoked                                      -7.662e-01  4.648e-01  3.221e-01 -2.379 0.017362 *  
Med.Statin.LLDno                                               4.173e-01  1.518e+00  2.269e-01  1.839 0.065863 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         4.291e-02  1.044e+00  2.805e-01  0.153 0.878414    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.097e-02  9.891e-01  5.097e-03 -2.151 0.031438 *  
BMI                                                            2.300e-02  1.023e+00  2.574e-02  0.893 0.371649    
MedHx_CVDyes                                                   8.184e-01  2.267e+00  2.360e-01  3.467 0.000526 ***
stenose0-49%                                                  -1.772e+01  2.022e-08  4.202e+03 -0.004 0.996636    
stenose50-70%                                                 -1.605e+00  2.009e-01  6.494e-01 -2.471 0.013471 *  
stenose70-90%                                                 -1.019e+00  3.610e-01  4.459e-01 -2.285 0.022342 *  
stenose90-99%                                                 -1.140e+00  3.198e-01  4.495e-01 -2.537 0.011196 *  
stenose100% (Occlusion)                                       -1.732e+01  3.014e-08  3.677e+03 -0.005 0.996242    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.816e+01  1.296e-08  6.156e+03 -0.003 0.997646    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.74,10.50] 1.251e+00  7.996e-01   0.85264    1.8343
Age                                                           1.042e+00  9.601e-01   1.01474    1.0691
Gendermale                                                    1.558e+00  6.419e-01   0.97896    2.4789
ORdate_year2002                                               2.821e+00  3.544e-01   0.32725   24.3257
ORdate_year2003                                               1.051e+00  9.511e-01   0.11438    9.6650
ORdate_year2004                                               1.484e+00  6.739e-01   0.17619   12.4987
ORdate_year2005                                               2.493e+00  4.011e-01   0.31325   19.8454
ORdate_year2006                                               2.724e+00  3.671e-01   0.35158   21.1102
ORdate_year2007                                               1.201e+00  8.325e-01   0.14222   10.1458
ORdate_year2008                                               2.640e+00  3.789e-01   0.33378   20.8733
ORdate_year2009                                               1.322e+00  7.563e-01   0.16218   10.7787
ORdate_year2010                                               1.614e+00  6.197e-01   0.19782   13.1639
ORdate_year2011                                               1.984e+00  5.039e-01   0.23533   16.7329
ORdate_year2012                                               2.947e+00  3.393e-01   0.37096   23.4171
ORdate_year2013                                               2.406e+00  4.156e-01   0.29825   19.4141
ORdate_year2014                                               1.794e+00  5.573e-01   0.21175   15.2045
ORdate_year2015                                               9.761e-01  1.024e+00   0.08719   10.9283
ORdate_year2016                                               1.491e+00  6.706e-01   0.13306   16.7106
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.835e-01  1.132e+00   0.46258    1.6874
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.912e+00  5.231e-01   1.28006    2.8547
SmokerStatusEx-smoker                                         5.944e-01  1.682e+00   0.38802    0.9105
SmokerStatusNever smoked                                      4.648e-01  2.152e+00   0.24721    0.8738
Med.Statin.LLDno                                              1.518e+00  6.588e-01   0.97301    2.3676
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.044e+00  9.580e-01   0.60237    1.8089
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.891e-01  1.011e+00   0.97926    0.9990
BMI                                                           1.023e+00  9.773e-01   0.97292    1.0762
MedHx_CVDyes                                                  2.267e+00  4.411e-01   1.42733    3.6005
stenose0-49%                                                  2.022e-08  4.946e+07   0.00000       Inf
stenose50-70%                                                 2.009e-01  4.977e+00   0.05627    0.7175
stenose70-90%                                                 3.610e-01  2.770e+00   0.15065    0.8653
stenose90-99%                                                 3.198e-01  3.127e+00   0.13250    0.7717
stenose100% (Occlusion)                                       3.014e-08  3.318e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.296e-08  7.717e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.747  (se = 0.021 )
Likelihood ratio test= 92.42  on 33 df,   p=2e-07
Wald test            = 77.31  on 33 df,   p=2e-05
Score (logrank) test = 99.29  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD163 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: CD163 
Effect size...............: 0.223635 
Standard error............: 0.19544 
Odds ratio (effect size)..: 1.251 
Lower 95% CI..............: 0.853 
Upper 95% CI..............: 1.834 
T-value...................: 1.144268 
P-value...................: 0.2525126 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [PTGDS]; 29 out of 35 target-of-interest.
   > cross tabulation of PTGDS-stratum.

[2.37,4.58) [4.58,9.91] 
        546         546 

   > fitting the model for PTGDS-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.58,9.91]  9.714e-02  1.102e+00  1.952e-01  0.498 0.618823    
Age                                                           4.020e-02  1.041e+00  1.331e-02  3.020 0.002524 ** 
Gendermale                                                    4.565e-01  1.578e+00  2.371e-01  1.926 0.054162 .  
ORdate_year2002                                               9.952e-01  2.705e+00  1.100e+00  0.905 0.365466    
ORdate_year2003                                               1.553e-02  1.016e+00  1.132e+00  0.014 0.989054    
ORdate_year2004                                               3.599e-01  1.433e+00  1.088e+00  0.331 0.740822    
ORdate_year2005                                               8.857e-01  2.425e+00  1.059e+00  0.837 0.402811    
ORdate_year2006                                               9.687e-01  2.634e+00  1.045e+00  0.927 0.354009    
ORdate_year2007                                               1.177e-01  1.125e+00  1.088e+00  0.108 0.913811    
ORdate_year2008                                               9.325e-01  2.541e+00  1.055e+00  0.884 0.376941    
ORdate_year2009                                               2.343e-01  1.264e+00  1.071e+00  0.219 0.826736    
ORdate_year2010                                               4.459e-01  1.562e+00  1.071e+00  0.416 0.677308    
ORdate_year2011                                               6.310e-01  1.879e+00  1.087e+00  0.580 0.561751    
ORdate_year2012                                               1.060e+00  2.886e+00  1.058e+00  1.002 0.316521    
ORdate_year2013                                               8.814e-01  2.414e+00  1.067e+00  0.826 0.408841    
ORdate_year2014                                               5.949e-01  1.813e+00  1.091e+00  0.545 0.585733    
ORdate_year2015                                              -4.871e-02  9.525e-01  1.233e+00 -0.040 0.968475    
ORdate_year2016                                               4.320e-01  1.540e+00  1.233e+00  0.350 0.726065    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.243e-01  8.831e-01  3.299e-01 -0.377 0.706225    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.514e-01  1.918e+00  2.046e-01  3.183 0.001457 ** 
SmokerStatusEx-smoker                                        -5.190e-01  5.951e-01  2.181e-01 -2.379 0.017346 *  
SmokerStatusNever smoked                                     -7.593e-01  4.680e-01  3.224e-01 -2.355 0.018531 *  
Med.Statin.LLDno                                              4.134e-01  1.512e+00  2.270e-01  1.821 0.068616 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        4.234e-02  1.043e+00  2.803e-01  0.151 0.879929    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.089e-02  9.892e-01  5.058e-03 -2.153 0.031328 *  
BMI                                                           2.174e-02  1.022e+00  2.585e-02  0.841 0.400258    
MedHx_CVDyes                                                  8.226e-01  2.276e+00  2.362e-01  3.483 0.000496 ***
stenose0-49%                                                 -1.671e+01  5.515e-08  2.559e+03 -0.007 0.994788    
stenose50-70%                                                -1.588e+00  2.044e-01  6.497e-01 -2.444 0.014528 *  
stenose70-90%                                                -1.010e+00  3.640e-01  4.471e-01 -2.260 0.023814 *  
stenose90-99%                                                -1.141e+00  3.194e-01  4.508e-01 -2.532 0.011342 *  
stenose100% (Occlusion)                                      -1.633e+01  8.070e-08  2.233e+03 -0.007 0.994165    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.717e+01  3.492e-08  3.721e+03 -0.005 0.996319    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.58,9.91] 1.102e+00  9.074e-01   0.75161    1.6158
Age                                                          1.041e+00  9.606e-01   1.01422    1.0685
Gendermale                                                   1.578e+00  6.335e-01   0.99187    2.5120
ORdate_year2002                                              2.705e+00  3.696e-01   0.31344   23.3507
ORdate_year2003                                              1.016e+00  9.846e-01   0.11043    9.3414
ORdate_year2004                                              1.433e+00  6.978e-01   0.16991   12.0878
ORdate_year2005                                              2.425e+00  4.124e-01   0.30445   19.3102
ORdate_year2006                                              2.634e+00  3.796e-01   0.33968   20.4312
ORdate_year2007                                              1.125e+00  8.890e-01   0.13347    9.4807
ORdate_year2008                                              2.541e+00  3.936e-01   0.32109   20.1053
ORdate_year2009                                              1.264e+00  7.911e-01   0.15506   10.3048
ORdate_year2010                                              1.562e+00  6.403e-01   0.19125   12.7557
ORdate_year2011                                              1.879e+00  5.321e-01   0.22306   15.8346
ORdate_year2012                                              2.886e+00  3.465e-01   0.36279   22.9533
ORdate_year2013                                              2.414e+00  4.142e-01   0.29815   19.5493
ORdate_year2014                                              1.813e+00  5.516e-01   0.21346   15.3952
ORdate_year2015                                              9.525e-01  1.050e+00   0.08504   10.6675
ORdate_year2016                                              1.540e+00  6.492e-01   0.13740   17.2693
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.831e-01  1.132e+00   0.46263    1.6857
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.918e+00  5.213e-01   1.28441    2.8645
SmokerStatusEx-smoker                                        5.951e-01  1.680e+00   0.38812    0.9126
SmokerStatusNever smoked                                     4.680e-01  2.137e+00   0.24877    0.8805
Med.Statin.LLDno                                             1.512e+00  6.614e-01   0.96893    2.3593
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.043e+00  9.585e-01   0.60231    1.8070
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.892e-01  1.011e+00   0.97941    0.9990
BMI                                                          1.022e+00  9.785e-01   0.97150    1.0751
MedHx_CVDyes                                                 2.276e+00  4.393e-01   1.43288    3.6168
stenose0-49%                                                 5.515e-08  1.813e+07   0.00000       Inf
stenose50-70%                                                2.044e-01  4.893e+00   0.05719    0.7302
stenose70-90%                                                3.640e-01  2.747e+00   0.15156    0.8744
stenose90-99%                                                3.194e-01  3.131e+00   0.13201    0.7727
stenose100% (Occlusion)                                      8.070e-08  1.239e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                3.492e-08  2.864e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.747  (se = 0.021 )
Likelihood ratio test= 91.36  on 33 df,   p=2e-07
Wald test            = 76.09  on 33 df,   p=3e-05
Score (logrank) test = 97.98  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PTGDS ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: PTGDS 
Effect size...............: 0.097138 
Standard error............: 0.195245 
Odds ratio (effect size)..: 1.102 
Lower 95% CI..............: 0.752 
Upper 95% CI..............: 1.616 
T-value...................: 0.497519 
P-value...................: 0.6188232 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [LGALS9]; 30 out of 35 target-of-interest.
   > cross tabulation of LGALS9-stratum.

[2.37, 6.27) [6.27,10.57] 
         546          546 

   > fitting the model for LGALS9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.27,10.57]  3.676e-01  1.444e+00  1.980e-01  1.857 0.063280 .  
Age                                                            4.259e-02  1.044e+00  1.340e-02  3.179 0.001476 ** 
Gendermale                                                     4.323e-01  1.541e+00  2.378e-01  1.818 0.069108 .  
ORdate_year2002                                                1.030e+00  2.800e+00  1.097e+00  0.938 0.348124    
ORdate_year2003                                               -6.467e-03  9.936e-01  1.131e+00 -0.006 0.995437    
ORdate_year2004                                                3.274e-01  1.387e+00  1.086e+00  0.302 0.763025    
ORdate_year2005                                                8.542e-01  2.349e+00  1.058e+00  0.808 0.419357    
ORdate_year2006                                                9.396e-01  2.559e+00  1.043e+00  0.901 0.367849    
ORdate_year2007                                                1.606e-01  1.174e+00  1.086e+00  0.148 0.882437    
ORdate_year2008                                                9.118e-01  2.489e+00  1.054e+00  0.865 0.386807    
ORdate_year2009                                                2.320e-01  1.261e+00  1.069e+00  0.217 0.828187    
ORdate_year2010                                                4.065e-01  1.501e+00  1.070e+00  0.380 0.704061    
ORdate_year2011                                                6.248e-01  1.868e+00  1.086e+00  0.575 0.565240    
ORdate_year2012                                                1.046e+00  2.847e+00  1.057e+00  0.990 0.322147    
ORdate_year2013                                                7.894e-01  2.202e+00  1.067e+00  0.740 0.459463    
ORdate_year2014                                                5.031e-01  1.654e+00  1.091e+00  0.461 0.644569    
ORdate_year2015                                               -5.316e-02  9.482e-01  1.231e+00 -0.043 0.965565    
ORdate_year2016                                                3.835e-01  1.467e+00  1.233e+00  0.311 0.755764    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.265e-01  8.812e-01  3.303e-01 -0.383 0.701813    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.756e-01  1.965e+00  2.054e-01  3.290 0.001003 ** 
SmokerStatusEx-smoker                                         -5.355e-01  5.854e-01  2.177e-01 -2.459 0.013915 *  
SmokerStatusNever smoked                                      -7.827e-01  4.572e-01  3.221e-01 -2.430 0.015113 *  
Med.Statin.LLDno                                               4.329e-01  1.542e+00  2.267e-01  1.909 0.056235 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         1.926e-02  1.019e+00  2.806e-01  0.069 0.945279    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.124e-02  9.888e-01  5.130e-03 -2.191 0.028433 *  
BMI                                                            2.213e-02  1.022e+00  2.580e-02  0.858 0.391091    
MedHx_CVDyes                                                   8.248e-01  2.281e+00  2.361e-01  3.493 0.000477 ***
stenose0-49%                                                  -1.775e+01  1.962e-08  4.139e+03 -0.004 0.996579    
stenose50-70%                                                 -1.627e+00  1.966e-01  6.513e-01 -2.497 0.012508 *  
stenose70-90%                                                 -1.050e+00  3.500e-01  4.479e-01 -2.344 0.019082 *  
stenose90-99%                                                 -1.174e+00  3.091e-01  4.520e-01 -2.597 0.009392 ** 
stenose100% (Occlusion)                                       -1.729e+01  3.106e-08  3.650e+03 -0.005 0.996221    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.818e+01  1.267e-08  6.119e+03 -0.003 0.997629    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.27,10.57] 1.444e+00  6.924e-01   0.97987    2.1289
Age                                                           1.044e+00  9.583e-01   1.01647    1.0713
Gendermale                                                    1.541e+00  6.490e-01   0.96673    2.4559
ORdate_year2002                                               2.800e+00  3.571e-01   0.32584   24.0649
ORdate_year2003                                               9.936e-01  1.006e+00   0.10828    9.1169
ORdate_year2004                                               1.387e+00  7.208e-01   0.16512   11.6578
ORdate_year2005                                               2.349e+00  4.256e-01   0.29553   18.6771
ORdate_year2006                                               2.559e+00  3.908e-01   0.33106   19.7801
ORdate_year2007                                               1.174e+00  8.516e-01   0.13971    9.8690
ORdate_year2008                                               2.489e+00  4.018e-01   0.31563   19.6246
ORdate_year2009                                               1.261e+00  7.930e-01   0.15524   10.2441
ORdate_year2010                                               1.501e+00  6.660e-01   0.18437   12.2283
ORdate_year2011                                               1.868e+00  5.354e-01   0.22209   15.7103
ORdate_year2012                                               2.847e+00  3.513e-01   0.35884   22.5834
ORdate_year2013                                               2.202e+00  4.541e-01   0.27195   17.8303
ORdate_year2014                                               1.654e+00  6.047e-01   0.19509   14.0194
ORdate_year2015                                               9.482e-01  1.055e+00   0.08487   10.5943
ORdate_year2016                                               1.467e+00  6.815e-01   0.13095   16.4440
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.812e-01  1.135e+00   0.46125    1.6835
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.965e+00  5.089e-01   1.31401    2.9390
SmokerStatusEx-smoker                                         5.854e-01  1.708e+00   0.38204    0.8970
SmokerStatusNever smoked                                      4.572e-01  2.187e+00   0.24315    0.8596
Med.Statin.LLDno                                              1.542e+00  6.486e-01   0.98856    2.4043
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.019e+00  9.809e-01   0.58823    1.7668
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.888e-01  1.011e+00   0.97893    0.9988
BMI                                                           1.022e+00  9.781e-01   0.97196    1.0754
MedHx_CVDyes                                                  2.281e+00  4.383e-01   1.43629    3.6240
stenose0-49%                                                  1.962e-08  5.096e+07   0.00000       Inf
stenose50-70%                                                 1.966e-01  5.087e+00   0.05485    0.7046
stenose70-90%                                                 3.500e-01  2.857e+00   0.14546    0.8420
stenose90-99%                                                 3.091e-01  3.235e+00   0.12746    0.7497
stenose100% (Occlusion)                                       3.106e-08  3.220e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.267e-08  7.895e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.747  (se = 0.022 )
Likelihood ratio test= 94.59  on 33 df,   p=7e-08
Wald test            = 78.45  on 33 df,   p=1e-05
Score (logrank) test = 101.1  on 33 df,   p=8e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LGALS9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: LGALS9 
Effect size...............: 0.367639 
Standard error............: 0.197951 
Odds ratio (effect size)..: 1.444 
Lower 95% CI..............: 0.98 
Upper 95% CI..............: 2.129 
T-value...................: 1.857222 
P-value...................: 0.06327953 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [ACKR1]; 31 out of 35 target-of-interest.
   > cross tabulation of ACKR1-stratum.

[2.37, 4.26) [4.26,11.26] 
         546          546 

   > fitting the model for ACKR1-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.26,11.26]  2.960e-01  1.345e+00  1.972e-01  1.501 0.133294    
Age                                                            4.006e-02  1.041e+00  1.325e-02  3.023 0.002504 ** 
Gendermale                                                     4.395e-01  1.552e+00  2.369e-01  1.855 0.063611 .  
ORdate_year2002                                                1.083e+00  2.955e+00  1.100e+00  0.985 0.324822    
ORdate_year2003                                                6.793e-02  1.070e+00  1.132e+00  0.060 0.952161    
ORdate_year2004                                                3.765e-01  1.457e+00  1.087e+00  0.346 0.729097    
ORdate_year2005                                                8.891e-01  2.433e+00  1.058e+00  0.840 0.400696    
ORdate_year2006                                                1.008e+00  2.739e+00  1.045e+00  0.965 0.334760    
ORdate_year2007                                                1.863e-01  1.205e+00  1.088e+00  0.171 0.864027    
ORdate_year2008                                                9.470e-01  2.578e+00  1.055e+00  0.898 0.369209    
ORdate_year2009                                                2.891e-01  1.335e+00  1.071e+00  0.270 0.787174    
ORdate_year2010                                                4.754e-01  1.609e+00  1.071e+00  0.444 0.657077    
ORdate_year2011                                                6.809e-01  1.976e+00  1.087e+00  0.626 0.531202    
ORdate_year2012                                                1.067e+00  2.908e+00  1.058e+00  1.009 0.312864    
ORdate_year2013                                                8.527e-01  2.346e+00  1.066e+00  0.800 0.423621    
ORdate_year2014                                                5.982e-01  1.819e+00  1.090e+00  0.549 0.583268    
ORdate_year2015                                               -3.924e-02  9.615e-01  1.232e+00 -0.032 0.974604    
ORdate_year2016                                                3.822e-01  1.465e+00  1.233e+00  0.310 0.756665    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.253e-01  8.822e-01  3.294e-01 -0.381 0.703546    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.604e-01  1.936e+00  2.047e-01  3.227 0.001252 ** 
SmokerStatusEx-smoker                                         -5.321e-01  5.874e-01  2.181e-01 -2.440 0.014698 *  
SmokerStatusNever smoked                                      -7.775e-01  4.596e-01  3.219e-01 -2.415 0.015733 *  
Med.Statin.LLDno                                               4.303e-01  1.538e+00  2.270e-01  1.896 0.058002 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         4.676e-02  1.048e+00  2.807e-01  0.167 0.867687    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.122e-02  9.888e-01  5.103e-03 -2.199 0.027853 *  
BMI                                                            2.265e-02  1.023e+00  2.584e-02  0.877 0.380598    
MedHx_CVDyes                                                   8.332e-01  2.301e+00  2.365e-01  3.523 0.000426 ***
stenose0-49%                                                  -1.673e+01  5.450e-08  2.550e+03 -0.007 0.994767    
stenose50-70%                                                 -1.594e+00  2.032e-01  6.491e-01 -2.455 0.014078 *  
stenose70-90%                                                 -1.026e+00  3.586e-01  4.465e-01 -2.297 0.021614 *  
stenose90-99%                                                 -1.165e+00  3.118e-01  4.503e-01 -2.588 0.009663 ** 
stenose100% (Occlusion)                                       -1.628e+01  8.468e-08  2.246e+03 -0.007 0.994216    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.719e+01  3.412e-08  3.745e+03 -0.005 0.996337    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.26,11.26] 1.345e+00  7.438e-01   0.91351    1.9789
Age                                                           1.041e+00  9.607e-01   1.01419    1.0683
Gendermale                                                    1.552e+00  6.444e-01   0.97541    2.4692
ORdate_year2002                                               2.955e+00  3.384e-01   0.34188   25.5383
ORdate_year2003                                               1.070e+00  9.343e-01   0.11632    9.8477
ORdate_year2004                                               1.457e+00  6.863e-01   0.17306   12.2693
ORdate_year2005                                               2.433e+00  4.110e-01   0.30591   19.3492
ORdate_year2006                                               2.739e+00  3.651e-01   0.35352   21.2216
ORdate_year2007                                               1.205e+00  8.301e-01   0.14292   10.1553
ORdate_year2008                                               2.578e+00  3.879e-01   0.32627   20.3699
ORdate_year2009                                               1.335e+00  7.490e-01   0.16375   10.8866
ORdate_year2010                                               1.609e+00  6.216e-01   0.19720   13.1239
ORdate_year2011                                               1.976e+00  5.062e-01   0.23449   16.6446
ORdate_year2012                                               2.908e+00  3.439e-01   0.36585   23.1115
ORdate_year2013                                               2.346e+00  4.263e-01   0.29057   18.9393
ORdate_year2014                                               1.819e+00  5.498e-01   0.21464   15.4119
ORdate_year2015                                               9.615e-01  1.040e+00   0.08588   10.7656
ORdate_year2016                                               1.465e+00  6.824e-01   0.13066   16.4356
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.822e-01  1.134e+00   0.46257    1.6825
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.936e+00  5.167e-01   1.29597    2.8908
SmokerStatusEx-smoker                                         5.874e-01  1.702e+00   0.38309    0.9007
SmokerStatusNever smoked                                      4.596e-01  2.176e+00   0.24451    0.8637
Med.Statin.LLDno                                              1.538e+00  6.503e-01   0.98552    2.3992
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.048e+00  9.543e-01   0.60449    1.8165
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.888e-01  1.011e+00   0.97900    0.9988
BMI                                                           1.023e+00  9.776e-01   0.97240    1.0760
MedHx_CVDyes                                                  2.301e+00  4.347e-01   1.44727    3.6570
stenose0-49%                                                  5.450e-08  1.835e+07   0.00000       Inf
stenose50-70%                                                 2.032e-01  4.922e+00   0.05693    0.7251
stenose70-90%                                                 3.586e-01  2.789e+00   0.14947    0.8603
stenose90-99%                                                 3.118e-01  3.207e+00   0.12900    0.7538
stenose100% (Occlusion)                                       8.468e-08  1.181e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 3.412e-08  2.931e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.745  (se = 0.022 )
Likelihood ratio test= 93.38  on 33 df,   p=1e-07
Wald test            = 77.81  on 33 df,   p=2e-05
Score (logrank) test = 99.93  on 33 df,   p=1e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' ACKR1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: ACKR1 
Effect size...............: 0.29604 
Standard error............: 0.197198 
Odds ratio (effect size)..: 1.345 
Lower 95% CI..............: 0.914 
Upper 95% CI..............: 1.979 
T-value...................: 1.501237 
P-value...................: 0.1332943 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [NT5DC2]; 32 out of 35 target-of-interest.
   > cross tabulation of NT5DC2-stratum.

[2.37,5.51) [5.51,9.43] 
        546         546 

   > fitting the model for NT5DC2-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.51,9.43]  1.904e-01  1.210e+00  2.006e-01  0.949 0.342583    
Age                                                           4.008e-02  1.041e+00  1.333e-02  3.008 0.002633 ** 
Gendermale                                                    4.664e-01  1.594e+00  2.376e-01  1.963 0.049609 *  
ORdate_year2002                                               1.063e+00  2.895e+00  1.102e+00  0.965 0.334696    
ORdate_year2003                                               4.956e-02  1.051e+00  1.132e+00  0.044 0.965085    
ORdate_year2004                                               3.881e-01  1.474e+00  1.088e+00  0.357 0.721250    
ORdate_year2005                                               9.025e-01  2.466e+00  1.059e+00  0.852 0.394023    
ORdate_year2006                                               1.014e+00  2.756e+00  1.046e+00  0.969 0.332362    
ORdate_year2007                                               1.821e-01  1.200e+00  1.090e+00  0.167 0.867298    
ORdate_year2008                                               1.001e+00  2.720e+00  1.058e+00  0.946 0.344136    
ORdate_year2009                                               2.856e-01  1.331e+00  1.071e+00  0.267 0.789805    
ORdate_year2010                                               4.813e-01  1.618e+00  1.071e+00  0.449 0.653267    
ORdate_year2011                                               6.759e-01  1.966e+00  1.088e+00  0.621 0.534363    
ORdate_year2012                                               1.082e+00  2.952e+00  1.058e+00  1.023 0.306457    
ORdate_year2013                                               8.939e-01  2.445e+00  1.065e+00  0.839 0.401190    
ORdate_year2014                                               5.904e-01  1.805e+00  1.091e+00  0.541 0.588265    
ORdate_year2015                                               3.210e-04  1.000e+00  1.234e+00  0.000 0.999792    
ORdate_year2016                                               4.349e-01  1.545e+00  1.233e+00  0.353 0.724226    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.183e-01  8.884e-01  3.298e-01 -0.359 0.719747    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.632e-01  1.941e+00  2.049e-01  3.236 0.001212 ** 
SmokerStatusEx-smoker                                        -5.000e-01  6.065e-01  2.181e-01 -2.292 0.021904 *  
SmokerStatusNever smoked                                     -7.483e-01  4.732e-01  3.227e-01 -2.319 0.020379 *  
Med.Statin.LLDno                                              4.102e-01  1.507e+00  2.268e-01  1.809 0.070469 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        3.977e-02  1.041e+00  2.801e-01  0.142 0.887088    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.132e-02  9.887e-01  5.095e-03 -2.221 0.026320 *  
BMI                                                           2.200e-02  1.022e+00  2.577e-02  0.854 0.393267    
MedHx_CVDyes                                                  8.230e-01  2.277e+00  2.362e-01  3.485 0.000492 ***
stenose0-49%                                                 -1.672e+01  5.492e-08  2.565e+03 -0.007 0.994800    
stenose50-70%                                                -1.579e+00  2.062e-01  6.493e-01 -2.432 0.015032 *  
stenose70-90%                                                -9.943e-01  3.700e-01  4.453e-01 -2.233 0.025543 *  
stenose90-99%                                                -1.124e+00  3.250e-01  4.495e-01 -2.500 0.012407 *  
stenose100% (Occlusion)                                      -1.629e+01  8.402e-08  2.245e+03 -0.007 0.994209    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.720e+01  3.398e-08  3.773e+03 -0.005 0.996363    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][5.51,9.43] 1.210e+00  8.266e-01   0.81643    1.7926
Age                                                          1.041e+00  9.607e-01   1.01406    1.0684
Gendermale                                                   1.594e+00  6.273e-01   1.00080    2.5396
ORdate_year2002                                              2.895e+00  3.454e-01   0.33396   25.1000
ORdate_year2003                                              1.051e+00  9.516e-01   0.11425    9.6651
ORdate_year2004                                              1.474e+00  6.784e-01   0.17485   12.4286
ORdate_year2005                                              2.466e+00  4.056e-01   0.30950   19.6448
ORdate_year2006                                              2.756e+00  3.628e-01   0.35486   21.4070
ORdate_year2007                                              1.200e+00  8.335e-01   0.14167   10.1606
ORdate_year2008                                              2.720e+00  3.676e-01   0.34212   21.6295
ORdate_year2009                                              1.331e+00  7.516e-01   0.16294   10.8658
ORdate_year2010                                              1.618e+00  6.180e-01   0.19818   13.2126
ORdate_year2011                                              1.966e+00  5.087e-01   0.23314   16.5754
ORdate_year2012                                              2.952e+00  3.388e-01   0.37081   23.4999
ORdate_year2013                                              2.445e+00  4.091e-01   0.30328   19.7059
ORdate_year2014                                              1.805e+00  5.541e-01   0.21288   15.2988
ORdate_year2015                                              1.000e+00  9.997e-01   0.08909   11.2312
ORdate_year2016                                              1.545e+00  6.473e-01   0.13791   17.3050
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.884e-01  1.126e+00   0.46542    1.6958
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.941e+00  5.152e-01   1.29893    2.9005
SmokerStatusEx-smoker                                        6.065e-01  1.649e+00   0.39552    0.9301
SmokerStatusNever smoked                                     4.732e-01  2.113e+00   0.25140    0.8905
Med.Statin.LLDno                                             1.507e+00  6.635e-01   0.96632    2.3506
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.041e+00  9.610e-01   0.60094    1.8018
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.887e-01  1.011e+00   0.97892    0.9987
BMI                                                          1.022e+00  9.782e-01   0.97189    1.0752
MedHx_CVDyes                                                 2.277e+00  4.391e-01   1.43353    3.6181
stenose0-49%                                                 5.492e-08  1.821e+07   0.00000       Inf
stenose50-70%                                                2.062e-01  4.850e+00   0.05775    0.7362
stenose70-90%                                                3.700e-01  2.703e+00   0.15459    0.8855
stenose90-99%                                                3.250e-01  3.077e+00   0.13466    0.7843
stenose100% (Occlusion)                                      8.402e-08  1.190e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                3.398e-08  2.943e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.748  (se = 0.021 )
Likelihood ratio test= 92.01  on 33 df,   p=2e-07
Wald test            = 76.84  on 33 df,   p=2e-05
Score (logrank) test = 98.67  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' NT5DC2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: NT5DC2 
Effect size...............: 0.190413 
Standard error............: 0.20063 
Odds ratio (effect size)..: 1.21 
Lower 95% CI..............: 0.816 
Upper 95% CI..............: 1.793 
T-value...................: 0.949074 
P-value...................: 0.3425827 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [TGFBI]; 33 out of 35 target-of-interest.
   > cross tabulation of TGFBI-stratum.

[2.37, 6.4) [6.40,10.8] 
        546         546 

   > fitting the model for TGFBI-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.40,10.8]  1.035e-01  1.109e+00  2.001e-01  0.517 0.604899    
Age                                                           4.040e-02  1.041e+00  1.335e-02  3.026 0.002479 ** 
Gendermale                                                    4.656e-01  1.593e+00  2.375e-01  1.960 0.049957 *  
ORdate_year2002                                               1.013e+00  2.753e+00  1.101e+00  0.920 0.357780    
ORdate_year2003                                               3.587e-02  1.037e+00  1.133e+00  0.032 0.974746    
ORdate_year2004                                               3.744e-01  1.454e+00  1.088e+00  0.344 0.730816    
ORdate_year2005                                               9.074e-01  2.478e+00  1.060e+00  0.856 0.391833    
ORdate_year2006                                               9.828e-01  2.672e+00  1.046e+00  0.940 0.347407    
ORdate_year2007                                               1.475e-01  1.159e+00  1.092e+00  0.135 0.892511    
ORdate_year2008                                               9.482e-01  2.581e+00  1.056e+00  0.898 0.369319    
ORdate_year2009                                               2.585e-01  1.295e+00  1.072e+00  0.241 0.809413    
ORdate_year2010                                               4.605e-01  1.585e+00  1.072e+00  0.430 0.667417    
ORdate_year2011                                               6.584e-01  1.932e+00  1.088e+00  0.605 0.545204    
ORdate_year2012                                               1.051e+00  2.859e+00  1.058e+00  0.993 0.320759    
ORdate_year2013                                               8.912e-01  2.438e+00  1.066e+00  0.836 0.403086    
ORdate_year2014                                               5.999e-01  1.822e+00  1.091e+00  0.550 0.582406    
ORdate_year2015                                              -4.906e-02  9.521e-01  1.232e+00 -0.040 0.968250    
ORdate_year2016                                               4.181e-01  1.519e+00  1.233e+00  0.339 0.734611    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.222e-01  8.850e-01  3.299e-01 -0.370 0.711122    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.537e-01  1.923e+00  2.048e-01  3.192 0.001411 ** 
SmokerStatusEx-smoker                                        -5.145e-01  5.978e-01  2.178e-01 -2.362 0.018186 *  
SmokerStatusNever smoked                                     -7.565e-01  4.693e-01  3.224e-01 -2.346 0.018967 *  
Med.Statin.LLDno                                              4.086e-01  1.505e+00  2.267e-01  1.802 0.071541 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        4.160e-02  1.042e+00  2.801e-01  0.149 0.881948    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.091e-02  9.891e-01  5.062e-03 -2.155 0.031156 *  
BMI                                                           2.286e-02  1.023e+00  2.583e-02  0.885 0.376192    
MedHx_CVDyes                                                  8.161e-01  2.262e+00  2.361e-01  3.457 0.000546 ***
stenose0-49%                                                 -1.672e+01  5.503e-08  2.563e+03 -0.007 0.994797    
stenose50-70%                                                -1.585e+00  2.050e-01  6.492e-01 -2.441 0.014644 *  
stenose70-90%                                                -1.001e+00  3.677e-01  4.454e-01 -2.246 0.024680 *  
stenose90-99%                                                -1.131e+00  3.227e-01  4.493e-01 -2.517 0.011827 *  
stenose100% (Occlusion)                                      -1.634e+01  8.018e-08  2.236e+03 -0.007 0.994170    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.719e+01  3.430e-08  3.751e+03 -0.005 0.996343    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.40,10.8] 1.109e+00  9.016e-01   0.74925    1.6417
Age                                                          1.041e+00  9.604e-01   1.01434    1.0688
Gendermale                                                   1.593e+00  6.278e-01   1.00009    2.5372
ORdate_year2002                                              2.753e+00  3.633e-01   0.31806   23.8221
ORdate_year2003                                              1.037e+00  9.648e-01   0.11249    9.5512
ORdate_year2004                                              1.454e+00  6.877e-01   0.17226   12.2757
ORdate_year2005                                              2.478e+00  4.036e-01   0.31052   19.7723
ORdate_year2006                                              2.672e+00  3.743e-01   0.34397   20.7548
ORdate_year2007                                              1.159e+00  8.629e-01   0.13644    9.8440
ORdate_year2008                                              2.581e+00  3.874e-01   0.32565   20.4579
ORdate_year2009                                              1.295e+00  7.722e-01   0.15852   10.5784
ORdate_year2010                                              1.585e+00  6.310e-01   0.19399   12.9477
ORdate_year2011                                              1.932e+00  5.177e-01   0.22885   16.3052
ORdate_year2012                                              2.859e+00  3.498e-01   0.35945   22.7422
ORdate_year2013                                              2.438e+00  4.102e-01   0.30184   19.6922
ORdate_year2014                                              1.822e+00  5.489e-01   0.21473   15.4583
ORdate_year2015                                              9.521e-01  1.050e+00   0.08503   10.6609
ORdate_year2016                                              1.519e+00  6.583e-01   0.13546   17.0340
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.850e-01  1.130e+00   0.46357    1.6895
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.923e+00  5.201e-01   1.28705    2.8719
SmokerStatusEx-smoker                                        5.978e-01  1.673e+00   0.39007    0.9162
SmokerStatusNever smoked                                     4.693e-01  2.131e+00   0.24947    0.8829
Med.Statin.LLDno                                             1.505e+00  6.646e-01   0.96483    2.3466
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.042e+00  9.593e-01   0.60206    1.8051
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.891e-01  1.011e+00   0.97938    0.9990
BMI                                                          1.023e+00  9.774e-01   0.97261    1.0763
MedHx_CVDyes                                                 2.262e+00  4.422e-01   1.42395    3.5921
stenose0-49%                                                 5.503e-08  1.817e+07   0.00000       Inf
stenose50-70%                                                2.050e-01  4.878e+00   0.05743    0.7317
stenose70-90%                                                3.677e-01  2.720e+00   0.15359    0.8802
stenose90-99%                                                3.227e-01  3.099e+00   0.13377    0.7785
stenose100% (Occlusion)                                      8.018e-08  1.247e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                3.430e-08  2.915e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.747  (se = 0.021 )
Likelihood ratio test= 91.38  on 33 df,   p=2e-07
Wald test            = 76.23  on 33 df,   p=3e-05
Score (logrank) test = 98.09  on 33 df,   p=2e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' TGFBI ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: TGFBI 
Effect size...............: 0.103531 
Standard error............: 0.20011 
Odds ratio (effect size)..: 1.109 
Lower 95% CI..............: 0.749 
Upper 95% CI..............: 1.642 
T-value...................: 0.517369 
P-value...................: 0.6048988 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [C1QC]; 34 out of 35 target-of-interest.
   > cross tabulation of C1QC-stratum.

[2.37, 6.64) [6.64,11.51] 
         546          546 

   > fitting the model for C1QC-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                    coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.64,11.51]  3.598e-01  1.433e+00  1.973e-01  1.823 0.068282 .  
Age                                                            4.130e-02  1.042e+00  1.328e-02  3.110 0.001869 ** 
Gendermale                                                     4.365e-01  1.547e+00  2.374e-01  1.839 0.065985 .  
ORdate_year2002                                                1.088e+00  2.967e+00  1.099e+00  0.990 0.322230    
ORdate_year2003                                                5.540e-02  1.057e+00  1.131e+00  0.049 0.960942    
ORdate_year2004                                                3.983e-01  1.489e+00  1.087e+00  0.367 0.713970    
ORdate_year2005                                                8.984e-01  2.456e+00  1.058e+00  0.850 0.395569    
ORdate_year2006                                                1.024e+00  2.785e+00  1.044e+00  0.981 0.326669    
ORdate_year2007                                                2.006e-01  1.222e+00  1.087e+00  0.185 0.853550    
ORdate_year2008                                                9.877e-01  2.685e+00  1.054e+00  0.937 0.348847    
ORdate_year2009                                                3.197e-01  1.377e+00  1.070e+00  0.299 0.765227    
ORdate_year2010                                                4.702e-01  1.600e+00  1.070e+00  0.439 0.660302    
ORdate_year2011                                                6.993e-01  2.012e+00  1.087e+00  0.643 0.520079    
ORdate_year2012                                                1.120e+00  3.065e+00  1.057e+00  1.059 0.289389    
ORdate_year2013                                                8.466e-01  2.332e+00  1.065e+00  0.795 0.426797    
ORdate_year2014                                                5.917e-01  1.807e+00  1.090e+00  0.543 0.587081    
ORdate_year2015                                                1.365e-02  1.014e+00  1.232e+00  0.011 0.991166    
ORdate_year2016                                                4.128e-01  1.511e+00  1.233e+00  0.335 0.737702    
ORdate_year2017                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                       NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                       NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                      -1.226e-01  8.846e-01  3.299e-01 -0.372 0.710157    
Hypertension.compositeyes                                             NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                         6.767e-01  1.967e+00  2.057e-01  3.290 0.001001 ** 
SmokerStatusEx-smoker                                         -5.355e-01  5.854e-01  2.181e-01 -2.456 0.014055 *  
SmokerStatusNever smoked                                      -7.697e-01  4.631e-01  3.223e-01 -2.388 0.016949 *  
Med.Statin.LLDno                                               4.394e-01  1.552e+00  2.267e-01  1.938 0.052655 .  
Med.Statin.LLDyes                                                     NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                         1.781e-02  1.018e+00  2.809e-01  0.063 0.949437    
Med.all.antiplateletyes                                               NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                      -1.121e-02  9.889e-01  5.113e-03 -2.192 0.028355 *  
BMI                                                            2.426e-02  1.025e+00  2.595e-02  0.935 0.349864    
MedHx_CVDyes                                                   8.243e-01  2.280e+00  2.363e-01  3.488 0.000486 ***
stenose0-49%                                                  -1.775e+01  1.952e-08  4.171e+03 -0.004 0.996605    
stenose50-70%                                                 -1.651e+00  1.918e-01  6.518e-01 -2.533 0.011302 *  
stenose70-90%                                                 -1.054e+00  3.486e-01  4.483e-01 -2.350 0.018761 *  
stenose90-99%                                                 -1.194e+00  3.030e-01  4.525e-01 -2.639 0.008326 ** 
stenose100% (Occlusion)                                       -1.731e+01  3.047e-08  3.651e+03 -0.005 0.996218    
stenoseNA                                                             NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                 -1.820e+01  1.245e-08  6.118e+03 -0.003 0.997626    
stenose70-99%                                                         NA         NA  0.000e+00     NA       NA    
stenose99                                                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                              exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][6.64,11.51] 1.433e+00  6.978e-01   0.97336    2.1097
Age                                                           1.042e+00  9.595e-01   1.01539    1.0696
Gendermale                                                    1.547e+00  6.463e-01   0.97158    2.4643
ORdate_year2002                                               2.967e+00  3.370e-01   0.34442   25.5656
ORdate_year2003                                               1.057e+00  9.461e-01   0.11511    9.7049
ORdate_year2004                                               1.489e+00  6.715e-01   0.17700   12.5309
ORdate_year2005                                               2.456e+00  4.072e-01   0.30904   19.5144
ORdate_year2006                                               2.785e+00  3.591e-01   0.35973   21.5596
ORdate_year2007                                               1.222e+00  8.182e-01   0.14525   10.2829
ORdate_year2008                                               2.685e+00  3.724e-01   0.34003   21.2037
ORdate_year2009                                               1.377e+00  7.264e-01   0.16891   11.2203
ORdate_year2010                                               1.600e+00  6.248e-01   0.19655   13.0313
ORdate_year2011                                               2.012e+00  4.970e-01   0.23896   16.9451
ORdate_year2012                                               3.065e+00  3.263e-01   0.38599   24.3341
ORdate_year2013                                               2.332e+00  4.289e-01   0.28898   18.8131
ORdate_year2014                                               1.807e+00  5.534e-01   0.21356   15.2915
ORdate_year2015                                               1.014e+00  9.864e-01   0.09054   11.3508
ORdate_year2016                                               1.511e+00  6.618e-01   0.13492   16.9235
ORdate_year2017                                                      NA         NA        NA        NA
ORdate_year2018                                                      NA         NA        NA        NA
ORdate_year2019                                                      NA         NA        NA        NA
ORdate_year2020                                                      NA         NA        NA        NA
ORdate_year2021                                                      NA         NA        NA        NA
ORdate_year2022                                                      NA         NA        NA        NA
Hypertension.compositeno                                      8.846e-01  1.130e+00   0.46338    1.6887
Hypertension.compositeyes                                            NA         NA        NA        NA
DiabetesStatusDiabetes                                        1.967e+00  5.083e-01   1.31469    2.9440
SmokerStatusEx-smoker                                         5.854e-01  1.708e+00   0.38177    0.8975
SmokerStatusNever smoked                                      4.631e-01  2.159e+00   0.24623    0.8712
Med.Statin.LLDno                                              1.552e+00  6.444e-01   0.99497    2.4200
Med.Statin.LLDyes                                                    NA         NA        NA        NA
Med.all.antiplateletno                                        1.018e+00  9.823e-01   0.58698    1.7654
Med.all.antiplateletyes                                              NA         NA        NA        NA
GFR_MDRD                                                      9.889e-01  1.011e+00   0.97899    0.9988
BMI                                                           1.025e+00  9.760e-01   0.97375    1.0780
MedHx_CVDyes                                                  2.280e+00  4.386e-01   1.43492    3.6234
stenose0-49%                                                  1.952e-08  5.123e+07   0.00000       Inf
stenose50-70%                                                 1.918e-01  5.213e+00   0.05347    0.6882
stenose70-90%                                                 3.486e-01  2.868e+00   0.14480    0.8395
stenose90-99%                                                 3.030e-01  3.300e+00   0.12484    0.7356
stenose100% (Occlusion)                                       3.047e-08  3.281e+07   0.00000       Inf
stenoseNA                                                            NA         NA        NA        NA
stenose50-99%                                                 1.245e-08  8.031e+07   0.00000       Inf
stenose70-99%                                                        NA         NA        NA        NA
stenose99                                                            NA         NA        NA        NA

Concordance= 0.747  (se = 0.022 )
Likelihood ratio test= 94.46  on 33 df,   p=8e-08
Wald test            = 78.33  on 33 df,   p=1e-05
Score (logrank) test = 101  on 33 df,   p=8e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' C1QC ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: C1QC 
Effect size...............: 0.359777 
Standard error............: 0.197339 
Odds ratio (effect size)..: 1.433 
Lower 95% CI..............: 0.973 
Upper 95% CI..............: 2.11 
T-value...................: 1.823138 
P-value...................: 0.06828248 
Sample size in model......: 944 
Number of events..........: 115 
   > processing [S100A9]; 35 out of 35 target-of-interest.
   > cross tabulation of S100A9-stratum.

[2.37,4.36) [4.36,9.23] 
        546         546 

   > fitting the model for S100A9-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] + 
    Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = TEMP.DF)

  n= 944, number of events= 115 
   (148 observations deleted due to missingness)

                                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.36,9.23]  3.307e-01  1.392e+00  1.985e-01  1.666 0.095727 .  
Age                                                           4.064e-02  1.041e+00  1.325e-02  3.067 0.002165 ** 
Gendermale                                                    4.268e-01  1.532e+00  2.375e-01  1.797 0.072299 .  
ORdate_year2002                                               1.057e+00  2.879e+00  1.098e+00  0.963 0.335615    
ORdate_year2003                                               6.709e-02  1.069e+00  1.132e+00  0.059 0.952736    
ORdate_year2004                                               3.800e-01  1.462e+00  1.087e+00  0.350 0.726667    
ORdate_year2005                                               9.100e-01  2.484e+00  1.057e+00  0.861 0.389479    
ORdate_year2006                                               9.665e-01  2.629e+00  1.043e+00  0.926 0.354310    
ORdate_year2007                                               2.196e-01  1.246e+00  1.088e+00  0.202 0.840081    
ORdate_year2008                                               9.829e-01  2.672e+00  1.054e+00  0.932 0.351240    
ORdate_year2009                                               3.051e-01  1.357e+00  1.070e+00  0.285 0.775595    
ORdate_year2010                                               4.711e-01  1.602e+00  1.070e+00  0.440 0.659832    
ORdate_year2011                                               6.934e-01  2.000e+00  1.087e+00  0.638 0.523640    
ORdate_year2012                                               1.052e+00  2.864e+00  1.057e+00  0.996 0.319481    
ORdate_year2013                                               8.620e-01  2.368e+00  1.065e+00  0.809 0.418313    
ORdate_year2014                                               5.618e-01  1.754e+00  1.090e+00  0.515 0.606323    
ORdate_year2015                                              -1.482e-02  9.853e-01  1.232e+00 -0.012 0.990405    
ORdate_year2016                                               4.066e-01  1.502e+00  1.232e+00  0.330 0.741472    
ORdate_year2017                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2018                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2019                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2020                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2021                                                      NA         NA  0.000e+00     NA       NA    
ORdate_year2022                                                      NA         NA  0.000e+00     NA       NA    
Hypertension.compositeno                                     -1.243e-01  8.831e-01  3.299e-01 -0.377 0.706239    
Hypertension.compositeyes                                            NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                        6.618e-01  1.938e+00  2.049e-01  3.230 0.001236 ** 
SmokerStatusEx-smoker                                        -5.153e-01  5.973e-01  2.176e-01 -2.368 0.017882 *  
SmokerStatusNever smoked                                     -7.611e-01  4.672e-01  3.220e-01 -2.364 0.018094 *  
Med.Statin.LLDno                                              4.422e-01  1.556e+00  2.277e-01  1.942 0.052127 .  
Med.Statin.LLDyes                                                    NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                        2.735e-02  1.028e+00  2.810e-01  0.097 0.922461    
Med.all.antiplateletyes                                              NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                     -1.147e-02  9.886e-01  5.141e-03 -2.232 0.025629 *  
BMI                                                           2.180e-02  1.022e+00  2.589e-02  0.842 0.399689    
MedHx_CVDyes                                                  8.198e-01  2.270e+00  2.366e-01  3.465 0.000531 ***
stenose0-49%                                                 -1.772e+01  2.010e-08  4.166e+03 -0.004 0.996606    
stenose50-70%                                                -1.614e+00  1.991e-01  6.501e-01 -2.483 0.013041 *  
stenose70-90%                                                -1.031e+00  3.567e-01  4.473e-01 -2.304 0.021197 *  
stenose90-99%                                                -1.145e+00  3.183e-01  4.503e-01 -2.542 0.011008 *  
stenose100% (Occlusion)                                      -1.729e+01  3.095e-08  3.679e+03 -0.005 0.996250    
stenoseNA                                                            NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                -1.823e+01  1.214e-08  6.269e+03 -0.003 0.997680    
stenose70-99%                                                        NA         NA  0.000e+00     NA       NA    
stenose99                                                            NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                                                             exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][4.36,9.23] 1.392e+00  7.184e-01   0.94330    2.0541
Age                                                          1.041e+00  9.602e-01   1.01478    1.0689
Gendermale                                                   1.532e+00  6.526e-01   0.96209    2.4407
ORdate_year2002                                              2.879e+00  3.474e-01   0.33454   24.7740
ORdate_year2003                                              1.069e+00  9.351e-01   0.11631    9.8324
ORdate_year2004                                              1.462e+00  6.839e-01   0.17366   12.3133
ORdate_year2005                                              2.484e+00  4.025e-01   0.31270   19.7355
ORdate_year2006                                              2.629e+00  3.804e-01   0.34006   20.3214
ORdate_year2007                                              1.246e+00  8.028e-01   0.14754   10.5164
ORdate_year2008                                              2.672e+00  3.742e-01   0.33833   21.1076
ORdate_year2009                                              1.357e+00  7.370e-01   0.16651   11.0554
ORdate_year2010                                              1.602e+00  6.243e-01   0.19660   13.0491
ORdate_year2011                                              2.000e+00  4.999e-01   0.23751   16.8494
ORdate_year2012                                              2.864e+00  3.492e-01   0.36083   22.7310
ORdate_year2013                                              2.368e+00  4.223e-01   0.29362   19.0956
ORdate_year2014                                              1.754e+00  5.702e-01   0.20703   14.8565
ORdate_year2015                                              9.853e-01  1.015e+00   0.08804   11.0262
ORdate_year2016                                              1.502e+00  6.659e-01   0.13412   16.8134
ORdate_year2017                                                     NA         NA        NA        NA
ORdate_year2018                                                     NA         NA        NA        NA
ORdate_year2019                                                     NA         NA        NA        NA
ORdate_year2020                                                     NA         NA        NA        NA
ORdate_year2021                                                     NA         NA        NA        NA
ORdate_year2022                                                     NA         NA        NA        NA
Hypertension.compositeno                                     8.831e-01  1.132e+00   0.46262    1.6857
Hypertension.compositeyes                                           NA         NA        NA        NA
DiabetesStatusDiabetes                                       1.938e+00  5.159e-01   1.29727    2.8959
SmokerStatusEx-smoker                                        5.973e-01  1.674e+00   0.38995    0.9150
SmokerStatusNever smoked                                     4.672e-01  2.141e+00   0.24853    0.8781
Med.Statin.LLDno                                             1.556e+00  6.426e-01   0.99594    2.4316
Med.Statin.LLDyes                                                   NA         NA        NA        NA
Med.all.antiplateletno                                       1.028e+00  9.730e-01   0.59248    1.7827
Med.all.antiplateletyes                                             NA         NA        NA        NA
GFR_MDRD                                                     9.886e-01  1.012e+00   0.97868    0.9986
BMI                                                          1.022e+00  9.784e-01   0.97148    1.0752
MedHx_CVDyes                                                 2.270e+00  4.405e-01   1.42771    3.6095
stenose0-49%                                                 2.010e-08  4.974e+07   0.00000       Inf
stenose50-70%                                                1.991e-01  5.023e+00   0.05567    0.7119
stenose70-90%                                                3.567e-01  2.803e+00   0.14846    0.8572
stenose90-99%                                                3.183e-01  3.142e+00   0.13167    0.7693
stenose100% (Occlusion)                                      3.095e-08  3.231e+07   0.00000       Inf
stenoseNA                                                           NA         NA        NA        NA
stenose50-99%                                                1.214e-08  8.235e+07   0.00000       Inf
stenose70-99%                                                       NA         NA        NA        NA
stenose99                                                           NA         NA        NA        NA

Concordance= 0.748  (se = 0.022 )
Likelihood ratio test= 93.91  on 33 df,   p=9e-08
Wald test            = 79.01  on 33 df,   p=1e-05
Score (logrank) test = 101.1  on 33 df,   p=8e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' S100A9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.

We have collected the following:
Dataset used..............: AERNASE.clin.targets 
Outcome analyzed..........: epmajor.3years 
Protein...................: S100A9 
Effect size...............: 0.330726 
Standard error............: 0.198523 
Odds ratio (effect size)..: 1.392 
Lower 95% CI..............: 0.943 
Upper 95% CI..............: 2.054 
T-value...................: 1.665929 
P-value...................: 0.09572746 
Sample size in model......: 944 
Number of events..........: 115

cat("- Edit the column names...\n")

- Edit the column names...

colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")

- Correct the variable types...

COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.targets.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")

- Writing results to Excel-file...

head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.targets.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")

- Removing intermediate files...

rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.targets.COX.results)

Correlations

We correlated plaque levels of the biomarkers.

Plaque CONVOCALS_downstream expression levels

library(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
# AERNASE.clin.targets.temp <- subset(AERNASE.clin.targets, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AERNASE.clin.targets.temp <- subset(AERNASE.clin.targets, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AERNASE.clin.targets.temp <- subset(AERNASE.clin.targets, 
                          select = c(gene_list_qc,
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AERNASE.clin.targets.temp$CalcificationPlaque <- as.numeric(AERNASE.clin.targets.temp$CalcificationPlaque)
AERNASE.clin.targets.temp$CollagenPlaque <- as.numeric(AERNASE.clin.targets.temp$CollagenPlaque)
AERNASE.clin.targets.temp$Fat10Perc <- as.numeric(AERNASE.clin.targets.temp$Fat10Perc)
AERNASE.clin.targets.temp$MAC_binned <- as.numeric(AERNASE.clin.targets.temp$MAC_binned)
AERNASE.clin.targets.temp$SMC_binned <- as.numeric(AERNASE.clin.targets.temp$SMC_binned)
AERNASE.clin.targets.temp$IPH <- as.numeric(AERNASE.clin.targets.temp$IPH)
AERNASE.clin.targets.temp$Symptoms.5G <- as.numeric(AERNASE.clin.targets.temp$Symptoms.5G)
AERNASE.clin.targets.temp$AsymptSympt <- as.numeric(AERNASE.clin.targets.temp$AsymptSympt)
AERNASE.clin.targets.temp$EP_major <- as.numeric(AERNASE.clin.targets.temp$EP_major)
AERNASE.clin.targets.temp$EP_composite <- as.numeric(AERNASE.clin.targets.temp$EP_composite)
# str(AERNASE.clin.targets.temp)
AERNASE.clin.targets.matrix.RANK <- as.matrix(AERNASE.clin.targets.temp)
rm(AERNASE.clin.targets.temp)

corr_biomarkers.rank <- round(cor(AERNASE.clin.targets.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
corr_biomarkers.rank

                       SCGB3A2   LIX1 IGSF9B    ALB OR10A4  RASEF  NEDD4  TCL1A FBXO15     F5 TMEM212  PTPRD CYP46A1 OR2T33 SORBS2
SCGB3A2                  1.000 -0.007  0.794  0.361  0.316  0.693  0.587 -0.018  0.480  0.664   0.505  0.183   0.375  0.466  0.355
LIX1                    -0.007  1.000 -0.042  0.294  0.326  0.240  0.241  0.948  0.347  0.194   0.277 -0.020   0.331  0.229  0.001
IGSF9B                   0.794 -0.042  1.000  0.222  0.160  0.540  0.409 -0.081  0.315  0.492   0.328  0.157   0.221  0.284  0.431
ALB                      0.361  0.294  0.222  1.000  0.934  0.600  0.774  0.254  0.905  0.773   0.919  0.422   0.920  0.867  0.098
OR10A4                   0.316  0.326  0.160  0.934  1.000  0.546  0.727  0.300  0.880  0.725   0.891  0.413   0.901  0.832  0.097
RASEF                    0.693  0.240  0.540  0.600  0.546  1.000  0.822  0.214  0.729  0.808   0.738  0.209   0.633  0.735  0.136
NEDD4                    0.587  0.241  0.409  0.774  0.727  0.822  1.000  0.213  0.846  0.888   0.900  0.332   0.810  0.920  0.123
TCL1A                   -0.018  0.948 -0.081  0.254  0.300  0.214  0.213  1.000  0.309  0.172   0.240 -0.060   0.288  0.195  0.030
FBXO15                   0.480  0.347  0.315  0.905  0.880  0.729  0.846  0.309  1.000  0.844   0.934  0.411   0.945  0.865  0.095
F5                       0.664  0.194  0.492  0.773  0.725  0.808  0.888  0.172  0.844  1.000   0.878  0.359   0.793  0.851  0.175
TMEM212                  0.505  0.277  0.328  0.919  0.891  0.738  0.900  0.240  0.934  0.878   1.000  0.416   0.922  0.942  0.082
PTPRD                    0.183 -0.020  0.157  0.422  0.413  0.209  0.332 -0.060  0.411  0.359   0.416  1.000   0.454  0.387  0.192
CYP46A1                  0.375  0.331  0.221  0.920  0.901  0.633  0.810  0.288  0.945  0.793   0.922  0.454   1.000  0.874  0.054
OR2T33                   0.466  0.229  0.284  0.867  0.832  0.735  0.920  0.195  0.865  0.851   0.942  0.387   0.874  1.000  0.052
SORBS2                   0.355  0.001  0.431  0.098  0.097  0.136  0.123  0.030  0.095  0.175   0.082  0.192   0.054  0.052  1.000
ITGA7                    0.645  0.112  0.527  0.690  0.635  0.758  0.906  0.089  0.755  0.856   0.813  0.403   0.708  0.820  0.290
FOS                     -0.022 -0.310  0.090 -0.338 -0.329 -0.274 -0.260 -0.272 -0.380 -0.223  -0.356 -0.103  -0.401 -0.328  0.256
HMOX1                   -0.270 -0.325 -0.183 -0.522 -0.498 -0.475 -0.512 -0.287 -0.586 -0.512  -0.566 -0.340  -0.591 -0.545 -0.041
LAPTM5                  -0.142 -0.299 -0.032 -0.495 -0.484 -0.403 -0.419 -0.252 -0.539 -0.407  -0.522 -0.298  -0.550 -0.485  0.115
MMP9                    -0.153 -0.362 -0.070 -0.436 -0.427 -0.373 -0.378 -0.323 -0.497 -0.373  -0.463 -0.273  -0.491 -0.427  0.029
                        ITGA7    FOS  HMOX1 LAPTM5   MMP9 SIGLEC1    FTL   CD14   HCST  TIMP3   CCL2   SAT1  CD163  PTGDS LGALS9  ACKR1
SCGB3A2                 0.645 -0.022 -0.270 -0.142 -0.153  -0.140 -0.107 -0.102 -0.148  0.019 -0.090 -0.102  0.008 -0.123 -0.125 -0.158
LIX1                    0.112 -0.310 -0.325 -0.299 -0.362  -0.342 -0.291 -0.317 -0.276 -0.406 -0.323 -0.372 -0.396 -0.289 -0.330 -0.299
IGSF9B                  0.527  0.090 -0.183 -0.032 -0.070  -0.046 -0.003  0.003 -0.054  0.119  0.018 -0.018  0.104 -0.027 -0.039 -0.089
ALB                     0.690 -0.338 -0.522 -0.495 -0.436  -0.460 -0.478 -0.476 -0.486 -0.351 -0.380 -0.395 -0.372 -0.450 -0.459 -0.380
OR10A4                  0.635 -0.329 -0.498 -0.484 -0.427  -0.432 -0.461 -0.458 -0.455 -0.360 -0.363 -0.373 -0.377 -0.434 -0.429 -0.352
RASEF                   0.758 -0.274 -0.475 -0.403 -0.373  -0.411 -0.379 -0.387 -0.424 -0.272 -0.367 -0.389 -0.283 -0.357 -0.400 -0.373
NEDD4                   0.906 -0.260 -0.512 -0.419 -0.378  -0.432 -0.397 -0.419 -0.453 -0.268 -0.365 -0.388 -0.293 -0.386 -0.429 -0.388
TCL1A                   0.089 -0.272 -0.287 -0.252 -0.323  -0.303 -0.242 -0.273 -0.224 -0.369 -0.278 -0.327 -0.371 -0.253 -0.283 -0.259
FBXO15                  0.755 -0.380 -0.586 -0.539 -0.497  -0.511 -0.520 -0.519 -0.541 -0.394 -0.443 -0.465 -0.413 -0.493 -0.510 -0.426
F5                      0.856 -0.223 -0.512 -0.407 -0.373  -0.404 -0.377 -0.387 -0.426 -0.226 -0.323 -0.344 -0.259 -0.358 -0.387 -0.356
TMEM212                 0.813 -0.356 -0.566 -0.522 -0.463  -0.495 -0.500 -0.506 -0.522 -0.369 -0.432 -0.447 -0.378 -0.466 -0.494 -0.421
PTPRD                   0.403 -0.103 -0.340 -0.298 -0.273  -0.259 -0.302 -0.260 -0.297 -0.101 -0.173 -0.216 -0.150 -0.246 -0.272 -0.196
CYP46A1                 0.708 -0.401 -0.591 -0.550 -0.491  -0.527 -0.530 -0.538 -0.552 -0.420 -0.464 -0.474 -0.430 -0.510 -0.522 -0.446
OR2T33                  0.820 -0.328 -0.545 -0.485 -0.427  -0.477 -0.470 -0.485 -0.506 -0.349 -0.412 -0.434 -0.352 -0.459 -0.479 -0.421
SORBS2                  0.290  0.256 -0.041  0.115  0.029   0.109  0.128  0.146  0.103  0.276  0.240  0.147  0.158  0.121  0.095  0.051
ITGA7                   1.000 -0.076 -0.453 -0.295 -0.292  -0.312 -0.265 -0.277 -0.342 -0.081 -0.196 -0.243 -0.152 -0.268 -0.304 -0.303
FOS                    -0.076  1.000  0.570  0.736  0.644   0.727  0.730  0.768  0.722  0.780  0.828  0.789  0.740  0.658  0.733  0.594
HMOX1                  -0.453  0.570  1.000  0.789  0.754   0.810  0.803  0.800  0.823  0.592  0.728  0.791  0.744  0.672  0.812  0.681
LAPTM5                 -0.295  0.736  0.789  1.000  0.883   0.909  0.953  0.958  0.954  0.769  0.855  0.911  0.881  0.791  0.946  0.691
MMP9                   -0.292  0.644  0.754  0.883  1.000   0.807  0.840  0.845  0.862  0.718  0.761  0.833  0.809  0.763  0.869  0.675
                       NT5DC2  TGFBI   C1QC S100A9 CalcificationPlaque CollagenPlaque Fat10Perc    IPH MAC_binned SMC_binned
SCGB3A2                -0.019 -0.019 -0.154 -0.108              -0.091          0.001    -0.197 -0.105     -0.047      0.070
LIX1                   -0.337 -0.387 -0.286 -0.274               0.055          0.077    -0.170 -0.083     -0.116      0.157
IGSF9B                  0.108  0.096 -0.044  0.017              -0.082         -0.020    -0.162 -0.075     -0.065      0.044
ALB                    -0.470 -0.439 -0.480 -0.471              -0.006          0.043    -0.206 -0.099     -0.157      0.074
OR10A4                 -0.467 -0.433 -0.467 -0.463               0.007          0.048    -0.203 -0.099     -0.135      0.092
RASEF                  -0.326 -0.335 -0.451 -0.370              -0.027         -0.015    -0.241 -0.083     -0.105      0.050
NEDD4                  -0.367 -0.369 -0.457 -0.388              -0.015          0.025    -0.207 -0.091     -0.120      0.053
TCL1A                  -0.287 -0.344 -0.244 -0.242               0.076          0.086    -0.164 -0.077     -0.101      0.172
FBXO15                 -0.490 -0.482 -0.539 -0.513               0.004          0.024    -0.217 -0.083     -0.143      0.091
F5                     -0.320 -0.318 -0.433 -0.370              -0.019          0.017    -0.241 -0.075     -0.135      0.074
TMEM212                -0.480 -0.466 -0.524 -0.495              -0.014          0.038    -0.227 -0.096     -0.134      0.091
PTPRD                  -0.239 -0.189 -0.293 -0.244               0.000         -0.003    -0.065 -0.030     -0.054     -0.035
CYP46A1                -0.532 -0.518 -0.551 -0.530               0.007          0.034    -0.191 -0.077     -0.136      0.074
OR2T33                 -0.458 -0.450 -0.502 -0.458              -0.003          0.021    -0.213 -0.095     -0.123      0.060
SORBS2                  0.252  0.266  0.110  0.157               0.030          0.024    -0.125 -0.078     -0.165      0.046
ITGA7                  -0.163 -0.172 -0.332 -0.246              -0.044          0.022    -0.190 -0.084     -0.114      0.049
FOS                     0.794  0.814  0.725  0.715              -0.067         -0.004     0.099  0.038      0.073     -0.084
HMOX1                   0.695  0.729  0.793  0.756              -0.102         -0.011     0.184  0.079      0.166     -0.085
LAPTM5                  0.862  0.897  0.925  0.884              -0.093         -0.004     0.159  0.004      0.116     -0.061
MMP9                    0.740  0.821  0.800  0.768              -0.112         -0.014     0.161 -0.010      0.131     -0.092
                       MAC_rankNorm SMC_rankNorm MAC_SMC_ratio_rank VesselDensity_rankNorm Symptoms.5G AsymptSympt EP_major EP_composite
SCGB3A2                      -0.058        0.101             -0.003                 -0.035      -0.055      -0.050   -0.073       -0.050
LIX1                         -0.039        0.196              0.056                  0.049      -0.046      -0.101    0.040        0.068
IGSF9B                       -0.047        0.091             -0.008                 -0.063       0.001      -0.011   -0.074       -0.061
ALB                          -0.075        0.147             -0.059                 -0.064      -0.126      -0.148   -0.016       -0.031
OR10A4                       -0.062        0.157             -0.062                 -0.049      -0.116      -0.137   -0.037       -0.056
RASEF                        -0.038        0.103              0.014                 -0.029      -0.070      -0.091   -0.029       -0.027
NEDD4                        -0.053        0.113             -0.022                 -0.038      -0.122      -0.134   -0.042       -0.052
TCL1A                        -0.042        0.185              0.057                  0.057      -0.038      -0.090    0.040        0.068
FBXO15                       -0.070        0.159             -0.020                 -0.042      -0.109      -0.127   -0.035       -0.047
F5                           -0.071        0.147             -0.035                 -0.037      -0.116      -0.135   -0.054       -0.046
TMEM212                      -0.065        0.168             -0.054                 -0.054      -0.125      -0.143   -0.033       -0.049
PTPRD                        -0.043        0.056             -0.021                 -0.082      -0.023      -0.030   -0.079       -0.052
CYP46A1                      -0.067        0.156             -0.044                 -0.048      -0.119      -0.139   -0.040       -0.044
OR2T33                       -0.048        0.128             -0.040                 -0.049      -0.141      -0.155   -0.045       -0.056
SORBS2                       -0.101        0.092             -0.030                 -0.064      -0.025      -0.032   -0.074       -0.050
ITGA7                        -0.081        0.099             -0.049                 -0.057      -0.107      -0.106   -0.048       -0.065
FOS                          -0.016       -0.112              0.020                  0.012       0.037       0.056    0.042       -0.005
HMOX1                         0.065       -0.177              0.042                  0.050       0.071       0.084    0.050        0.027
LAPTM5                        0.028       -0.132              0.051                  0.019       0.064       0.077    0.050        0.019
MMP9                          0.030       -0.170              0.049                  0.019       0.046       0.063    0.048        0.014
 [ reached getOption("max.print") -- omitted 29 rows ]

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AERNASE.clin.targets.matrix.RANK, use = "pairwise.complete.obs", method = "spearman", exact = FALSE)

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
                method = "square", 
                type = "lower",
                title = "Cross biomarker correlations", 
                show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
                ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
                show.diag = TRUE,
                hc.order = FALSE, 
                lab = FALSE,
                digits = 3,
                # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
                colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
pdf(paste0(PLOT_loc, "/", Today, ".cross_biomarker_correlations.simple.pdf"))

  p1
dev.off()

quartz_off_screen 
                2

library(data.table)
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

NA


# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
library("PerformanceAnalytics")

chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AERNASE.clin.targets.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)

pdf(paste0(PLOT_loc, "/", Today, ".cross_biomarker_correlations.complex.pdf"))

  chart.Correlation.new(AERNASE.clin.targets.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
dev.off()

quartz_off_screen 
                2

NA

# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
# install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
# library(GGally)
 
# From the help page:

p1 <- ggpairs(AERNASE.clin.targets,
              columns = c(gene_list_qc, TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
              columnLabels = c(gene_list_qc,
                               "Calcification", "Collagen", "Fat 10%", "IPH", 
                               "Macrophages (binned)", "SMC (binned)", 
                               "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                               "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
              method = c("spearman"),
              # ggplot2::aes(colour = Gender),
              progress = FALSE)
p1
pdf(paste0(PLOT_loc, "/", Today, ".cross_biomarker_correlations.advanced.pdf"))

  p1
dev.off()

quartz_off_screen 
                2

Saving datasets

For the purpose of downstream analyses we save the AEDB.CEA object, and the RNAseq objects.


saveRDS(AERNASE.clin.targets, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.targets.df.RDS"))
saveRDS(AERNASE.targets, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.targets.se.RDS"))
saveRDS(AERNASE.targets.genedata, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.targets.genedata.RDS"))

Session information

Version:      v1.2.2
Last update:  2024-10-18
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to analyse targets from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_

_S_

_C_

_W_

**Changes log**
* v1.2.2 Textual fixes.
* v1.2.1 Fixed saveRDS to save targets, SE, genedata and clinical data.
* v1.2.0 Update to study database; update to bulk RNAseq data (deeper sequenced).
* v1.0.1 Small fixes with respect to filterin genes.
* v1.0.0 Inital version.

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS 15.1

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
 [1] stats4    grid      tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] MASS_7.3-61                             RColorBrewer_1.1-3                      Seurat_5.1.0                           
 [4] SeuratObject_5.0.2                      sp_2.1-4                                BiocManager_1.30.25                    
 [7] magrittr_2.0.3                          rmarkdown_2.28                          annotables_0.2.0                       
[10] EnhancedVolcano_1.22.0                  ggrepel_0.9.6                           AnnotationFilter_1.28.0                
[13] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 mygene_1.40.0                           txdbmaker_1.0.1                        
[16] org.Hs.eg.db_3.19.1                     DESeq2_1.44.0                           SummarizedExperiment_1.34.0            
[19] MatrixGenerics_1.16.0                   matrixStats_1.4.1                       GenomicFeatures_1.56.0                 
[22] GenomicRanges_1.56.2                    AnnotationDbi_1.66.0                    Biobase_2.64.0                         
[25] GenomeInfoDb_1.40.1                     IRanges_2.38.1                          S4Vectors_0.42.1                       
[28] BiocGenerics_0.50.0                     Hmisc_5.1-3                             survminer_0.4.9                        
[31] survival_3.7-0                          GGally_2.2.1                            PerformanceAnalytics_2.0.4             
[34] xts_0.14.0                              zoo_1.8-12                              ggcorrplot_0.1.4.999                   
[37] corrr_0.4.4                             reshape2_1.4.4                          meta_7.0-0                             
[40] metadat_1.2-0                           qqman_0.1.9                             sjlabelled_1.2.0                       
[43] patchwork_1.3.0.9000                    labelled_2.13.0                         sjPlot_2.8.16                          
[46] UpSetR_1.4.0                            ggpubr_0.6.0.999                        forestplot_3.1.5                       
[49] abind_1.4-8                             checkmate_2.3.2                         pheatmap_1.0.12                        
[52] devtools_2.4.5                          usethis_3.0.0                           BlandAltmanLeh_0.3.1                   
[55] tableone_0.13.2                         openxlsx_4.2.7.1                        haven_2.5.4                            
[58] eeptools_1.2.5                          DT_0.33                                 knitr_1.48                             
[61] lubridate_1.9.3                         forcats_1.0.0                           stringr_1.5.1                          
[64] purrr_1.0.2                             tibble_3.2.1                            ggplot2_3.5.1                          
[67] tidyverse_2.0.0                         data.table_1.16.2                       naniar_1.1.0                           
[70] tidylog_1.1.0                           tidyr_1.3.1                             dplyr_1.1.4                            
[73] optparse_1.7.5                          readr_2.1.5                             pander_0.6.5                           
[76] R.utils_2.12.3                          R.oo_1.26.0                             R.methodsS3_1.8.2                      
[79] worcs_0.1.15                            credentials_2.0.2                      

loaded via a namespace (and not attached):
  [1] igraph_2.0.3             ica_1.0-3                plotly_4.10.4            Formula_1.2-5            zlibbioc_1.50.0         
  [6] gert_2.1.4               tidyselect_1.2.1         bit_4.5.0                lattice_0.22-6           rjson_0.2.23            
 [11] blob_1.2.4               urlchecker_1.0.1         S4Arrays_1.4.1           parallel_4.4.1           png_0.1-8               
 [16] tinytex_0.53             cli_3.6.3                ProtGenerics_1.36.0      askpass_1.2.1            sjstats_0.19.0          
 [21] goftest_1.2-3            openssl_2.2.2            textshaping_0.4.0        BiocIO_1.14.0            uwot_0.2.2              
 [26] curl_5.2.3               mime_0.12                evaluate_1.0.1           leiden_0.4.3.1           gsubfn_0.7              
 [31] stringi_1.8.4            backports_1.5.0          XML_3.99-0.17            httpuv_1.6.15            rappdirs_0.3.3          
 [36] splines_4.4.1            getopt_1.20.4            KMsurv_0.1-5             ggbeeswarm_0.7.2         sctransform_0.4.1       
 [41] sessioninfo_1.2.2        DBI_1.2.3                jquerylib_0.1.4          withr_3.0.1              class_7.3-22            
 [46] systemfonts_1.1.0        lmtest_0.9-40            rtracklayer_1.64.0       htmlwidgets_1.6.4        fs_1.6.4                
 [51] biomaRt_2.60.1           labeling_0.4.3           gh_1.4.1                 SparseArray_1.4.8        ranger_0.16.0           
 [56] reticulate_1.39.0        XVector_0.44.0           UCSC.utils_1.0.0         timechange_0.3.0         fansi_1.0.6             
 [61] calibrate_1.7.7          RSpectra_0.16-2          irlba_2.3.5.1            ggrastr_1.0.2            commonmark_1.9.2        
 [66] fastDummies_1.7.4        ellipsis_0.3.2           lazyeval_0.2.2           yaml_2.3.10              scattermore_1.2         
 [71] crayon_1.5.3             RcppAnnoy_0.0.22         progressr_0.14.0         later_1.3.2              ggridges_0.5.6          
 [76] codetools_0.2-20         base64enc_0.1-3          profvis_0.4.0            KEGGREST_1.44.1          Rtsne_0.17              
 [81] limma_3.60.6             Rsamtools_2.20.0         filelock_1.0.3           rticles_0.27             foreign_0.8-87          
 [86] sqldf_0.4-11             pkgconfig_2.0.3          xml2_1.3.6               spatstat.univar_3.0-1    mathjaxr_1.6-0          
 [91] GenomicAlignments_1.40.0 spatstat.sparse_3.1-0    viridisLite_0.4.2        performance_0.12.3       xtable_1.8-4            
 [96] car_3.1-3                plyr_1.8.9               httr_1.4.7               globals_0.16.3           sys_3.4.3               
[101] pkgbuild_1.4.4           beeswarm_0.4.0           htmlTable_2.4.3          broom_1.0.7              nlme_3.1-166            
[106] dbplyr_2.5.0             survMisc_0.5.6           crosstalk_1.2.1          ggeffects_1.7.2          lme4_1.1-35.5           
[111] digest_0.6.37            numDeriv_2016.8-1.1      Matrix_1.7-0             farver_2.1.2             tzdb_0.4.0              
[116] rpart_4.1.23             glue_1.8.0               cachem_1.1.0             BiocFileCache_2.12.0     polyclip_1.10-7         
[121] generics_0.1.3           Biostrings_2.72.1        visdat_0.6.0             CompQuadForm_1.4.3       presto_1.0.0            
[126] proto_1.0.0              survey_4.4-2             parallelly_1.38.0        ggtext_0.1.2             statmod_1.5.0           
[131] pkgload_1.4.0            arm_1.14-4               RcppHNSW_0.6.0           ragg_1.3.3               carData_3.0-5           
[136] minqa_1.2.8              pbapply_1.7-2            httr2_1.0.5              spam_2.11-0              utf8_1.2.4              
[141] mitools_2.4              sjmisc_2.8.10            datawizard_0.13.0        ggsignif_0.6.4           gridExtra_2.3           
[146] shiny_1.9.1              GenomeInfoDbData_1.2.12  clisymbols_1.2.0         RCurl_1.98-1.16          memoise_2.0.1           
[151] scales_1.3.0             future_1.34.0            RANN_2.6.2               renv_1.0.11              km.ci_0.5-6             
[156] spatstat.data_3.1-2      rstudioapi_0.16.0        cluster_2.1.6            spatstat.utils_3.1-0     hms_1.1.3               
[161] fitdistrplus_1.2-1       munsell_0.5.1            cowplot_1.1.3            colorspace_2.1-1         rlang_1.1.4             
[166] quadprog_1.5-8           dotCall64_1.2            xfun_0.48                prereg_0.6.0             coda_0.19-4.1           
[171] e1071_1.7-16             metafor_4.6-0            remotes_2.5.0            ggsci_3.2.0              bitops_1.0-9            
[176] promises_1.3.0           RSQLite_2.3.7            DelayedArray_0.30.1      proxy_0.4-27             compiler_4.4.1          
[181] prettyunits_1.2.0        boot_1.3-31              listenv_0.9.1            Rcpp_1.0.13              tensor_1.5              
[186] progress_1.2.3           gridtext_0.1.5           BiocParallel_1.38.0      insight_0.20.5           spatstat.random_3.3-2   
[191] R6_2.5.1                 fastmap_1.2.0            rstatix_0.7.2            vipor_0.4.7              ROCR_1.0-11             
[196] ggstats_0.7.0            vcd_1.4-13               nnet_7.3-19              gtable_0.3.5             KernSmooth_2.23-24      
[201] miniUI_0.1.1.1           deldir_2.0-4             htmltools_0.5.8.1        bit64_4.5.2              spatstat.explore_3.3-2  
[206] lifecycle_1.0.4          zip_2.3.1                nloptr_2.1.1             restfulr_0.0.15          sass_0.4.9              
[211] vctrs_0.6.5              spatstat.geom_3.3-3      future.apply_1.11.2      bslib_0.8.0              pillar_1.9.0            
[216] locfit_1.5-9.10          jsonlite_1.8.9           markdown_1.13            chron_2.3-61

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))

---
title: "Main bulkRNAseq analyses"
subtitle: Accompanying 'CONVOCALS_downstream'
author: '[Sander W. van der Laan, PhD](https://vanderlaanand.science) | s.w.vanderlaan[at]gmail[dot]com'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
    highlight: tango
mainfont: Helvetica
editor_options:
  chunk_output_type: inline
bibliography: references.bib
knit: worcs::cite_all
---

# General Setup
```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
source("scripts/local.system.R")
```

```{r Source functions}
source("scripts/functions.R")
```

```{r loading_packages, message=FALSE, warning=FALSE}
source("scripts/pack02.packages.R")
```

```{r Setting: Colors}
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
source("scripts/colors.R")
```

```{r setup_notebook, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```


# Main analyses

Here we perform the main analyses. We first load the prepared RNAseq data, and extract only the relevant genes. 

## Targets

Here we obtain data from the `r TRAIT_OF_INTEREST` in plaques.

```{r targets, message=FALSE, warning=FALSE}
library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/targets.xlsx"), sheet = "Genes")

gene_list <- unlist(gene_list_df$Gene)
gene_list

```

### Filter genes

In case some genes are not available in our data we could filter them here.

```{r list target genes}
gene_list
```

This is to filter the list from genes that are not in the data.

- _ND6_ ==> _MTND6_, not found
- _TRIM49D1_ ==> _TRIM49DP_, not found
- _ND4L_ ==> _MTND4L_, not found
- _ATP8_ ==> _MTATP8_, not found
- _RNASE1_ ==> _RNS1_, not found


```{r Visualisation: preparation}

gene_list_rm <- c("ND6", "TRIM49D1", "ND4L", "ATP8", "RNASE1")

temp = gene_list[!gene_list %in% gene_list_rm]

gene_list_qc <- c(temp)

# gene_list_qc <- gene_list
# 
# for debug
# gene_list_qc_replace <- c("MRTFA")


```


# Loading data

We simply load the previously saved `RDS`-file and extract the clinical and RNAseq data from that.

```{r LoadAEDB}
# AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.CEA.RDS"))
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20241017.",PROJECTNAME,".AEDB.CEA.RDS"))
AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)

# AERNASE <- readRDS(file = paste0(OUT_loc, "/", Today, ".AERNAScomboSEvst.CEA.1092pts.SE.after_qc.IC_academic.RDS"))
AERNASE <- readRDS(file = paste0(OUT_loc, "/20241017.AERNAScomboSEvst.CEA.1092pts.SE.after_qc.IC_academic.RDS"))

# AERNASE.targets <- readRDS(file = paste0(OUT_loc, "/", Today, ".AERNAScomboSE_target.CEA.1092pts.SE.after_qc.IC_academic.RDS"))
AERNASE.targets <- readRDS(file = paste0(OUT_loc, "/20241017.AERNAScomboSE_target.CEA.1092pts.SE.after_qc.IC_academic.RDS"))

```


```{r}
row.names(AERNASE.targets) <- rowData(AERNASE.targets)$symbol
temp <- as_tibble(t(assay(AERNASE.targets)), rownames = "study_number")

AERNASE.clin.targets <- as.data.frame(merge(colData(AERNASE.targets), temp, by.x = "study_number", by.y = "study_number", sort = TRUE))
rm(temp)
dim(AERNASE.clin.targets)
head(AERNASE.clin.targets)
```


Getting some summary statistics.
```{r}
cat("Average expression of a random selection of 1000 genes.\n")
set.seed(141619)
mean(rowMeans(as_tibble(assay(AERNASE)) %>% dplyr::sample_n(1000)))

cat("\nAverage expression of target genes.\n")
rowMeans(assay(AERNASE.targets))

cat("\nGene information.\n")
rowRanges(AERNASE.targets)

AERNASE.targets.genedata <- as_tibble(rowRanges(AERNASE.targets))

AERNASE.targets.genedata
```


# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]
13. Year of surgery [`ORdate_year`] as we discovered in Van Lammeren _et al._ the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

## Models

We will analyze the data through four different models

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque `r TRAIT_OF_INTEREST` levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

### Quantitative traits

We inspect the plaque characteristics, and `inverse-rank normal transformation` continuous phenotypes.

```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AERNASE.clin.targets$MAC_rankNorm)

min_macmean <- min(AERNASE.clin.targets$MAC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

ggpubr::gghistogram(AERNASE.clin.targets, "MAC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AERNASE.clin.targets$SMC_rankNorm)

min_smcmean <- min(AERNASE.clin.targets$SMC_rankNorm, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

ggpubr::gghistogram(AERNASE.clin.targets, "SMC_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AERNASE.clin.targets$VesselDensity_rankNorm)

min_vesseldensity <- min(AERNASE.clin.targets$VesselDensity_rankNorm, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

ggpubr::gghistogram(AERNASE.clin.targets, "VesselDensity_rankNorm", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

Given their strong correlation, we also introduce a macrophages/smooth muscle cell ratio. This is a proxy of the extend to which a plaque is inflammed ('unstable') as compared to 'stable'. 

```{r CrossSec: plaques - transformations and visualisations continuous MAC-SMC}

AERNASE.clin.targets$MAC_SMC_ratio <- AERNASE.clin.targets$MAC_rankNorm / AERNASE.clin.targets$SMC_rankNorm

AERNASE.clin.targets$MAC_SMC_ratio_rank  <- qnorm((rank(AERNASE.clin.targets$MAC_SMC_ratio, na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.targets$MAC_SMC_ratio)))


cat("Summary of data.\n")
summary(AERNASE.clin.targets$MAC_rankNorm)
summary(AERNASE.clin.targets$SMC_rankNorm)
summary(AERNASE.clin.targets$MAC_SMC_ratio_rank)

ggpubr::gghistogram(AERNASE.clin.targets, "MAC_SMC_ratio_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "macrophages/smooth muscle cells ratio",
                    xlab = "inverse-rank normalized", 
                    ggtheme = theme_minimal())

```


### Binary traits
```{r CrossSec: plaques - transformations and visualisations binary}
library(dplyr)
# calcification
cat("Summary of data.\n")
summary(AERNASE.clin.targets$Calc.bin)
contrasts(AERNASE.clin.targets$Calc.bin)

AERNASE.clin.targets$CalcificationPlaque <- as.factor(AERNASE.clin.targets$Calc.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(CalcificationPlaque)) %>%
  dplyr::group_by(Gender, CalcificationPlaque) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AERNASE.clin.targets$Collagen.bin)
contrasts(AERNASE.clin.targets$Collagen.bin)

AERNASE.clin.targets$CollagenPlaque <- as.factor(AERNASE.clin.targets$Collagen.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(CollagenPlaque)) %>%
  dplyr::group_by(Gender, CollagenPlaque) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AERNASE.clin.targets$Fat.bin_10)
contrasts(AERNASE.clin.targets$Fat.bin_10)

AERNASE.clin.targets$Fat10Perc <- as.factor(AERNASE.clin.targets$Fat.bin_10)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(Fat10Perc)) %>%
  dplyr::group_by(Gender, Fat10Perc) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages binned
cat("Summary of data.\n")
summary(AERNASE.clin.targets$Macrophages.bin)
contrasts(AERNASE.clin.targets$Macrophages.bin)

AERNASE.clin.targets$MAC_binned <- as.factor(AERNASE.clin.targets$Macrophages.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(MAC_binned)) %>%
  dplyr::group_by(Gender, MAC_binned) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)



# SMC binned
cat("Summary of data.\n")
summary(AERNASE.clin.targets$SMC.bin)
contrasts(AERNASE.clin.targets$SMC.bin)

AERNASE.clin.targets$SMC_binned <- as.factor(AERNASE.clin.targets$SMC.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(SMC_binned)) %>%
  dplyr::group_by(Gender, SMC_binned) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)




# IPH
cat("Summary of data.\n")
summary(AERNASE.clin.targets$IPH.bin)
contrasts(AERNASE.clin.targets$IPH.bin)

AERNASE.clin.targets$IPH <- as.factor(AERNASE.clin.targets$IPH.bin)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(IPH)) %>%
  dplyr::group_by(Gender, IPH) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AERNASE.clin.targets$AsymptSympt)
contrasts(AERNASE.clin.targets$AsymptSympt)

AERNASE.clin.targets$AsymptSympt <- as.factor(AERNASE.clin.targets$AsymptSympt)

df <- AERNASE.clin.targets %>%
  dplyr::filter(!is.na(AsymptSympt)) %>%
  dplyr::group_by(Gender, AsymptSympt) %>%
dplyr::summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```



## Prepare modeling

In this section we make some variables to assist with analysis.

We fix the *diabetes* status variable.

```{r FixDiabetes, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"DiabetesStatus"] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == -999] <- NA
AEDB.CEA$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB.CEA$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB.CEA)

table(AEDB.CEA$DM.composite, AEDB.CEA$DiabetesStatus)
# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$DiabetesStatus <- to_factor(AEDB.CEA.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)

```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}
# AEDB.CEA$CAD_history
# AEDB.CEA$Stroke_history
# AEDB.CEA$Peripheral.interv

# Fix diabetes
attach(AEDB.CEA)
AEDB.CEA[,"MedHx_CVD"] <- NA
AEDB.CEA$MedHx_CVD[CAD_history == "No history CAD" | Stroke_history == 0 | Peripheral.interv == "no"] <- "No"
AEDB.CEA$MedHx_CVD[CAD_history == "History CAD" | Stroke_history == 1 | Peripheral.interv == "yes"] <- "yes"
detach(AEDB.CEA)

table(AEDB.CEA$CAD_history)
table(AEDB.CEA$Stroke_history)
table(AEDB.CEA$Peripheral.interv)
table(AEDB.CEA$MedHx_CVD)

# AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
# AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
# AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
# AEDB.CEA.temp$AlcoholUse <- to_factor(AEDB.CEA.temp$AlcoholUse)
# 
# DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
# 
# rm(AEDB.CEA.temp)


```


```{r}
AERNASE.clin.targets$DiabetesStatus <- NULL
AERNASE.clin.targets$MedHx_CVD <- NULL

AERNASE.clin.targets <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "dateok", "DiabetesStatus", "MedHx_CVD")), 
                            by.x = "study_number", by.y = "STUDY_NUMBER", sort = TRUE)

AERNASE.clin.targets$MedHx_CVD <- as_factor(AERNASE.clin.targets$MedHx_CVD)
AERNASE.clin.targets$DiabetesStatus <- as_factor(AERNASE.clin.targets$DiabetesStatus)

```


```{r CrossSec: plaques - setup regression }

study.samplesize = nrow(AERNASE.clin.targets) # study.samplesize is an expected variable in the GLM.BIN() GLM.CON() functions

TRAITS.TARGET.RANK = c(gene_list_qc)

TRAITS.CON.RANK = c("MAC_rankNorm", "SMC_rankNorm", "MAC_SMC_ratio_rank", "VesselDensity_rankNorm")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AERNASE.clin.targets$ORdate_epoch <- as.numeric(AERNASE.clin.targets$dateok)
AERNASE.clin.targets$ORdate_year <- AERNASE.clin.targets$ORyear
AERNASE.clin.targets$StudyName <- to_factor(AERNASE.clin.targets$StudyName)

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
summary(AERNASE.clin.targets$ORdate_epoch)

cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")
table(AERNASE.clin.targets$ORdate_year)
# COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch", "StudyName")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")
# str(AERNASE.clin.targets)
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

```

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

```{r}
detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

```

Analysis of continuous/quantitative plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, paged.print=FALSE}
library(dplyr)
library(MASS)

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))

cat("Running linear regression...\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    # print(model_step)
    # print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Con.Uni.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year,
    #           data  =  currentDF, family = binomial(link = "logit"))
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch,
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    # print(model_step)
    # print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the `inverse-rank normalized` data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plaque `r TRAIT_OF_INTEREST` expression levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    
    fit <- lm(currentDF[,TARGET] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
              Hypertension.composite + DiabetesStatus + SmokerStatus + 
              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
              MedHx_CVD + stenose, 
              data = currentDF) 
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    # print(model_step)
    # print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Con.Multi.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plaque `r TRAIT_OF_INTEREST` levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, paged.print=FALSE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    # print(model_step)
    # print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".PlaquePhenotypes.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque `r TRAIT_OF_INTEREST` expression levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

```{r CrossSec: symptoms - logistic regression MODEL1 RANK}
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, family = binomial(link = "logit"))

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: symptoms - logistic regression MODEL2 RANK}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)) {
  TARGET = TRAITS.TARGET.RANK[target_of_interest]
  cat(paste0("\nAnalysis of ",TARGET,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.targets %>%
      dplyr::select(., TARGET, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_year + 
    #              Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #              Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #              MedHx_CVD + stenose, 
    #            data  =  currentDF, family = binomial(link = "logit"))
    
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,TARGET] + Age + Gender + ORdate_epoch + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AERNASE.clin.targets", TARGET, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"),
           rowNames = FALSE, colNmes = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque `r TRAIT_OF_INTEREST` expression levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death.



```{r}
AERNASE.clin.targets <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", "max.followup", 
                                                        "FU.cutt.off.3years", 
                                                        "ep_major_t_3years"
                                                        # , "ep_stroke_t_3years", "ep_coronary_t_3years",
                                                        # "ep_cvdeath_t_3years",
                                                        # "epstroke.3years", "epcoronary.3years", "epcvdeath.3years"
                                                        )), 
                            by.x = "study_number", by.y = "STUDY_NUMBER", sort = TRUE)

```


```{r Calculate new FU cut-offs: maximum FU}
AERNASE.clin.targets.temp <- subset(AERNASE.clin.targets,  select = c("study_number", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years"))
require(labelled)
AERNASE.clin.targets.temp$Gender <- to_factor(AERNASE.clin.targets.temp$Gender)
AERNASE.clin.targets.temp$Hospital <- to_factor(AERNASE.clin.targets.temp$Hospital)
AERNASE.clin.targets.temp$Artery_summary <- to_factor(AERNASE.clin.targets.temp$Artery_summary)

DT::datatable(AERNASE.clin.targets.temp[1:10,], caption = "Excerpt of the whole AERNASE.clin.targets.", rownames = FALSE)

rm(AERNASE.clin.targets.temp)

```



### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, target_of_interest){
  cat("Summarizing Cox regression results for '", target_of_interest ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(target_of_interest, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, target_of_interest, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", target_of_interest, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

# times = c("ep_major_t_3years", 
#           "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")
# 
# endpoints = c("epmajor.3years", 
#               "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

times = c("ep_major_t_3years")

endpoints = c("epmajor.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AERNASE.clin.targets[,events]))
  print(table(AERNASE.clin.targets[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AERNASE.clin.targets[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AERNASE.clin.targets, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AERNASE.clin.targets.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

##### Model 1
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.targets)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.targets.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.targets.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.targets.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.targets", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.targets.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.targets.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.targets.COX.results)

```

##### Model 2
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AERNASE.clin.targets)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
  
  for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
    cat(paste0("   > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.targets.survival.",ep,".2G.",
                               TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AERNASE.clin.targets.Cox.",ep,".2G.",
                               # Today,".AERNASE.clin.targets.Cox.",ep,".4G.",
                               TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.targets", ep, TRAITS.TARGET.RANK[target_of_interest])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AERNASE.clin.targets.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.targets.COX.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           rowNames = FALSE, colNames = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.targets.COX.results)


```


# Correlations
We correlated plaque levels of the biomarkers.

## Plaque `r TRAIT_OF_INTEREST` expression levels

```{r}
library(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
# AERNASE.clin.targets.temp <- subset(AERNASE.clin.targets, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AERNASE.clin.targets.temp <- subset(AERNASE.clin.targets, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AERNASE.clin.targets.temp <- subset(AERNASE.clin.targets, 
                          select = c(gene_list_qc,
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AERNASE.clin.targets.temp$CalcificationPlaque <- as.numeric(AERNASE.clin.targets.temp$CalcificationPlaque)
AERNASE.clin.targets.temp$CollagenPlaque <- as.numeric(AERNASE.clin.targets.temp$CollagenPlaque)
AERNASE.clin.targets.temp$Fat10Perc <- as.numeric(AERNASE.clin.targets.temp$Fat10Perc)
AERNASE.clin.targets.temp$MAC_binned <- as.numeric(AERNASE.clin.targets.temp$MAC_binned)
AERNASE.clin.targets.temp$SMC_binned <- as.numeric(AERNASE.clin.targets.temp$SMC_binned)
AERNASE.clin.targets.temp$IPH <- as.numeric(AERNASE.clin.targets.temp$IPH)
AERNASE.clin.targets.temp$Symptoms.5G <- as.numeric(AERNASE.clin.targets.temp$Symptoms.5G)
AERNASE.clin.targets.temp$AsymptSympt <- as.numeric(AERNASE.clin.targets.temp$AsymptSympt)
AERNASE.clin.targets.temp$EP_major <- as.numeric(AERNASE.clin.targets.temp$EP_major)
AERNASE.clin.targets.temp$EP_composite <- as.numeric(AERNASE.clin.targets.temp$EP_composite)
# str(AERNASE.clin.targets.temp)
AERNASE.clin.targets.matrix.RANK <- as.matrix(AERNASE.clin.targets.temp)
rm(AERNASE.clin.targets.temp)

```


```{r CrossSampleType Correlations}
corr_biomarkers.rank <- round(cor(AERNASE.clin.targets.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AERNASE.clin.targets.matrix.RANK, use = "pairwise.complete.obs", method = "spearman", exact = FALSE)
```


```{r}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
                method = "square", 
                type = "lower",
                title = "Cross biomarker correlations", 
                show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
                ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
                show.diag = TRUE,
                hc.order = FALSE, 
                lab = FALSE,
                digits = 3,
                # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
                colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
pdf(paste0(PLOT_loc, "/", Today, ".cross_biomarker_correlations.simple.pdf"))
  p1
dev.off()

```


```{r Correlations table}
library(data.table)
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

```


```{r Correlations alternative visual 1, message=FALSE, warning=FALSE}

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
library("PerformanceAnalytics")

chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AERNASE.clin.targets.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)

pdf(paste0(PLOT_loc, "/", Today, ".cross_biomarker_correlations.complex.pdf"))
  chart.Correlation.new(AERNASE.clin.targets.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
dev.off()
  
```


```{r Correlations alternative visual 2, message=FALSE, warning=FALSE}
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
# install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
# library(GGally)
 
# From the help page:

p1 <- ggpairs(AERNASE.clin.targets,
              columns = c(gene_list_qc, TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
              columnLabels = c(gene_list_qc,
                               "Calcification", "Collagen", "Fat 10%", "IPH", 
                               "Macrophages (binned)", "SMC (binned)", 
                               "Macrophages", "SMC", "Macrophage/SMC", "Vessel density",
                               "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
              method = c("spearman"),
              # ggplot2::aes(colour = Gender),
              progress = FALSE)
p1
pdf(paste0(PLOT_loc, "/", Today, ".cross_biomarker_correlations.advanced.pdf"))
  p1
dev.off()
```
# Saving datasets

For the purpose of downstream analyses we save the `AEDB.CEA` object, and the RNAseq objects.

```{r}

saveRDS(AERNASE.clin.targets, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.targets.df.RDS"))
saveRDS(AERNASE.targets, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.targets.se.RDS"))
saveRDS(AERNASE.targets.genedata, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.targets.genedata.RDS"))
```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.2.2
    Last update:  2024-10-18
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse targets from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.2.2 Textual fixes.
    * v1.2.1 Fixed saveRDS to save targets, SE, genedata and clinical data.
    * v1.2.0 Update to study database; update to bulk RNAseq data (deeper sequenced).
    * v1.0.1 Small fixes with respect to filterin genes.
    * v1.0.0 Inital version.
    

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
```

+-----------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2024 Sander W. van der Laan | s.w.vanderlaan[at]gmail[dot]com | [vanderlaanand.science](https://vanderlaanand.science).</sup> |
+-----------------------------------------------------------------------------------------------------------------------------------------+



Main bulkRNAseq analyses

Accompanying ‘CONVOCALS_downstream’

Sander W. van der Laan, PhD | s.w.vanderlaan[at]gmail[dot]com

2024-10-18

General Setup

Main analyses

Targets

Filter genes

Loading data

Analyses

Covariates & other variables

Models

A. Cross-sectional analysis plaque phenotypes

Quantitative traits

Binary traits

Prepare modeling

Model 1

Quantitative plaque traits

Binary plaque traits

Model 2

Quantitative plaque traits

Binary plaque traits

B. Cross-sectional analysis symptoms

Model 1

Model 2

C. Longitudinal analysis secondary clinical outcome

Sanity checks

Cox regressions

3 years follow-up

Model 1

Model 2

Correlations

Plaque CONVOCALS_downstream expression levels

Saving datasets

Session information

Saving environment